Infrastructures doi: 10.3390/infrastructures11070221

Authors: Mojtaba Harati John W. van de Lindt

Tsunami fragility modeling plays a central role in probabilistic coastal risk assessment; however, representing structural vulnerability under near-field tsunami conditions remains challenging due to complex hydrodynamic loading, strong spatial variability, and the presence of pre-existing earthquake damage. This paper advances near-field tsunami fragility modeling through three specific contributions, each bridging simulation-based methods and empirical damage survey observations. First, it demonstrates how a successive earthquake–tsunami simulation framework can generate conditional fragility surfaces that explicitly account for pre-existing seismic damage without relying on statistically intractable probabilistic decompositions. Second, it develops and validates a distance-dependent intensity-shifting approach—derived from analysis of the 2011 Great East Japan tsunami survey dataset—that adapts baseline fragility curves to near-field and near-coast conditions in a physically interpretable and practically deployable manner. Third, it establishes an explicit cross-validation pathway between simulation-derived fragility surfaces and empirical damage observations through machine-learning-assisted feature importance analysis, a connection largely absent from prior literature. Together, these contributions provide a physically consistent and data-informed foundation for near-field tsunami fragility modeling that is directly applicable—as a methodological framework—to loss and resilience estimation platforms such as IN-CORE and HAZUS and to risk-informed coastal infrastructure design in subduction-zone regions, subject to typology-specific calibration; the simulation results are demonstrated for a US Reinforced Concrete (RC) moment-frame archetype and the empirical results for Japanese wood-frame construction, so direct quantitative application to other structural typologies requires recalibration of the respective model components.

Infrastructures doi: 10.3390/infrastructures11070220

Authors: Liang Chen Jialin Yang Fengbo Liu Jiming Xie Mingli Li

Driving simulation systems play a critical role in the “human-vehicle-road-environment” ecosystem of road traffic, where their effectiveness is fundamental for advancing scientific research. This study proposes a comprehensive evaluation framework for such systems, employing a Mul-Bayes-LSTM model to analyze multidimensional data encompassing drivers’ biopsychological and behavioral characteristics. The evaluation process integrates Bayesian hyperparameter optimization to enhance model performance, with rank correlation and R2 as key indicators of model fit. The gray correlation analysis, integrated entropy method, and CRITIC analysis are utilized for weighting these indicators, ensuring robust assessment. The overall evaluation index is derived using entropy and CRITIC methods to provide a comprehensive measure of simulation effectiveness. The results from experimental validation indicate that driver-specific parameters obtained from the test simulator closely align with behavioral variables in risk scenarios, confirming the system’s applicability for research in traffic perception. The research results can evaluate the effectiveness of driving simulators based on the driver’s perception level, which has certain significance for promoting the development and application of driving simulation systems.

Infrastructures doi: 10.3390/infrastructures11070219

Authors: Nawal Louzi Mahmoud AlJamal Mohammad Q. Al-Jamal

Sustainable and resilient road infrastructure requires the early identification of pavement deterioration mechanisms that emerge under complex urban traffic conditions, particularly at signalized intersections where repeated stop–go operations, queue persistence, and lane-wise freight concentration generate highly nonuniform structural loading. However, most existing intelligent transportation studies emphasize crash prediction, traffic-state estimation, or mobility optimization, while the infrastructure-performance consequences of freight-dominant interrupted flow remain insufficiently addressed. To support proactive pavement management and resilient urban road operation, this study proposes a traffic simulation-driven deep learning framework for predicting lane-level pavement deterioration under freight loading and stop–go urban traffic conditions. A high-resolution PTV Vissim 2024 microscopic simulation environment was developed for a four-leg signalized urban intersection, and a structured multi-scenario design was used to generate progressively increasing operational stress regimes, ranging from baseline flow to freight-dominant oversaturated operation. The resulting lane-wise dataset integrates direct traffic variables with pavement-oriented descriptors, including the Lane Freight Loading Index (LFLI), Stop–Go Severity Index (SGSI), ESAL proxy, queue persistence, and Loading Asymmetry Index (LAI). To learn the complex relationship between traffic operation and infrastructure degradation, a new Freight-Aware Lane Interaction Transformer Network (FLIT-Net) is introduced. The proposed model combines feature embedding, lane-interaction self-attention, freight-aware gating, residual refinement, and multi-task regression to jointly predict rutting risk, fatigue-cracking risk, and the Pavement Deterioration Index (PDI). Experimental results show that FLIT-Net outperforms MLP, CNN, LSTM, Bi-LSTM, and generic Transformer baselines, achieving RMSE/MAE/R2 values of 0.041/0.032/0.9687 for rutting risk, 0.044/0.034/0.9635 for fatigue-cracking risk, and 0.031/0.024/0.9824 for PDI. Sensitivity and scenario-wise analyses further confirm that deterioration increases monotonically with freight intensity, stop–go severity, and queue persistence, highlighting the importance of lane-resolved deterioration intelligence for sustainable maintenance prioritization. The proposed framework bridges traffic microsimulation, pavement-oriented feature engineering, and freight-aware deep learning, providing a decision-support basis for improving the performance, safety, and resilience of urban pavement infrastructure.

Infrastructures doi: 10.3390/infrastructures11070218

Authors: Fabrizio Scozzese

Flood-induced scour remains one of the leading causes of bridge failure, yet the chain linking flood hazard to bridge decisions is still commonly treated as a sequence of disconnected tasks. This review examines that chain using uncertainty as a unifying interpretive framework, synthesizing the recent literature on non-stationary flood hazard assessment, bridge-scale hydraulics, scour processes and predictive models, scour monitoring, monitoring-informed forecasting, structural vulnerability, and risk-informed decision-making. The review synthesizes the state of the art across all these stages of the chain, highlighting how the dominant uncertainty changes along it: climate and hydrologic variability upstream; model-form, sediment, and parameter uncertainty in scour prediction; measurement noise and inverse-inference uncertainty in monitoring; and threshold and consequence uncertainty in closure, retrofit, and network-level decisions. Although major advances have been achieved in probabilistic modelling, machine learning, hybrid physics-informed methods, and multimodal sensing, most published frameworks still transfer deterministic outputs from one stage to the next. As a result, uncertainty is rarely propagated consistently to the decision level. The main value of this review lies in making the chain’s weak interfaces explicit, in showing how uncertainty propagation can serve as a unifying framework across otherwise disconnected literatures, and in identifying which methodological directions are most promising for connecting prediction, monitoring, and decision support into a coherent end-to-end probabilistic chain supporting climate-resilient bridge management.

Infrastructures doi: 10.3390/infrastructures11070217

Authors: Yehia Miky Mahmoud Abo El-Wafa Mohamed A. Badran Hilal Hassan Ahmed S. Eisa

High-speed railway embankments constructed over karst-prone ground conditions are often challenged by weak soils and subsurface cavities, which can lead to instability and excessive settlement. This study presents a full-scale field investigation conducted in the El-Gharbaniyat area, west of Alexandria, Egypt, where a pile–cap ground improvement system was implemented to support a high-speed railway embankment founded on clayey and silty soils overlying fractured limestone. A comprehensive site investigation program was performed, including 28 boreholes and integrated geophysical surveys using Electrical Resistivity Tomography (ERT) and Seismic Tomography (ST), enabling improved identification of weak zones and cavity-prone formations. Based on these findings, a pile–cap system was designed using reinforced concrete piles of 0.60 m diameter and an average length of 29 m, arranged in a 4 × 4 m grid and capped with reinforced concrete footings to ensure efficient load transfer to deeper competent strata. The system performance was validated through laboratory testing and full-scale in situ pile load tests. The average 28-day compressive strength of 122 tested piles reached approximately 50 MPa, exceeding the design value by approximately 30%. Load test results showed settlements ranging from 1.08 to 2.76 mm at the working load (2200 kN) and 2.16 to 5.10 mm at the maximum load (3300 kN), all well below allowable limits. Comparative evaluation indicated that the proposed system achieves significant material savings (>90%), lower treatment cost (150 USD/m2), reduced carbon emission (5.7 t per pile), and shorter construction duration (7 h per pile). These findings confirm that the pile–cap system provides a robust, cost-effective, and environmentally efficient solution for ground improvement in karst environments.

Infrastructures doi: 10.3390/infrastructures11070215

Authors: Mengjun Chen Wuping Ran Jing Zhang Long Cheng Qianqian Qiu Linkun Jia Yaohan Su

Desert expressway corridors exposed to strong wind hazards often rely on single high-grade routes, with limited alternatives, high detour costs, and low network redundancy. These constraints make it difficult to maintain traffic operation resilience through route substitution alone. Taking the Hami–Tuyugou section of the G30 Lianhuo Expressway in Xinjiang, China, as a case study, this study investigates the formation and evaluation of traffic operation resilience in a wind-hazard-affected, low-redundancy desert expressway corridor. A hierarchical indicator system was constructed with four first-level, fourteen second-level, and thirty-one third-level indicators. Fuzzy DEMATEL(Decision Making Trial and Evaluation Laboratory)–ISM(Interpretive Structural Modeling) was used to identify causal relationships and hierarchical transmission paths; fuzzy DANP(DEMATEL-based Analytic Network Process)–AHP(Analytic Hierarchy Process) was applied to determine indicator weights; and a cloud model was employed to evaluate the overall resilience level. The results show that institutional adaptability, organizational learning, monitoring and information support, and multi-actor collaboration are the main upstream drivers. The corridor was evaluated as Grade IV, indicating a relatively high resilience level approaching Grade V. Sensitivity analyses confirm the robustness of the substantive conclusion. The findings suggest that, under low-redundancy conditions, resilience depends less on structural redundancy and more on adaptive governance, information support, and coordinated response.

Infrastructures doi: 10.3390/infrastructures11070216

Authors: Chan Huang Chuanchong Wei

Conductive heat flux (G) at pavement surfaces plays a vital role in managing internal temperature variations. G can be calculated either as the residual of solar absorption, heat convection, and long-wave radiation, or as the product of thermal conductivity and the temperature gradient near the surface. Both methods, however, are subject to uncertainties due to measurement parameters. For the two methods, this study formulates the uncertainty of the conductive heat flux at the pavement surface. The experiment was designed to measure pavement interior temperatures and external weather data so that the uncertainties of the two methods can be quantified and compared. It was found that ∆G estimated by the residual method is significantly higher than that calculated using conductivity and temperature gradient. The key factors influencing ∆G in the residual method, in order, are wind speed, incident solar radiation, and reflectivity, with other factors such as surface and air temperatures, relative humidity, and emissivity having minimal impact. In contrast, the primary contributors to ∆G in the conductivity and temperature gradient method are the temperature gradient and thermal conductivity. The residual method is crucial for predicting pavement temperatures when no pre-installed temperature sensors are available, and enhancing wind speed measurement precision can significantly reduce the uncertainty of G. The study finds that the approach of estimating G through conductivity and temperature gradient showed lower uncertainty than the residual method, particularly in complex urban environments.

Infrastructures doi: 10.3390/infrastructures11070214

Authors: Dinghua Zhang Yuanyuan Guo Qingqing Yuan Hongyang Xu Zirong Ni Xiao Liu Lei Gao

The increasing global demand for marine energy resources, coupled with the deployment of offshore oil and gas pipelines and submarine power cables, highlights the requirement for reliable subsea infrastructure. To protect these assets from environmental hazards and anthropogenic disturbances, seabed burial via trenching is widely adopted, with submarine trenchers serving as the main installation equipment. Trenching involves excavating a trench on the seabed to place pipelines, cables, or other subsea infrastructure. These operations involve complex soil–tool interactions that fundamentally govern cutting resistance, trench-wall stability, and overall equipment performance. Specifically, distinct engineering challenges arise across different trencher configurations: plough trenchers often encounter complex seabed structures, jet-type trenchers are prone to trench sidewall collapse, and mechanical trenchers face cutting difficulties in hard clay. A thorough understanding of these interactions is therefore critical for resolving operational challenges and optimizing trencher efficiency in engineering practice. To deeply understand these type-specific issues, this review summarizes the geomechanical problems associated with various trenching technologies, synthesizes recent research advances from analytical frameworks, physical experiments, and numerical simulations, and identifies existing knowledge gaps. By consolidating these findings, the paper provides a reference for addressing trencher-related engineering challenges, supporting equipment optimization, and facilitating the deployment of offshore energy transmission networks.

Infrastructures doi: 10.3390/infrastructures11070213

Authors: Mohsen Zaker Esteghamati Shiva Baddipalli

The quest for seismic resiliency requires designing for performance objectives beyond life safety. Functional recovery is an emerging objective often defined as the time required to restore a building’s basic functionality to the pre-event level. Nevertheless, quantifying functional recovery is a complex, computationally intensive process that is challenging to integrate into a standard design workflow. This study develops a machine learning (ML) model to map design and geometric features of steel special moment-resisting frames (SMRFs) to their functional recovery under two hazard levels: design-basis (DBE) and maximum considered (MCE) earthquakes. First, functional recovery time was quantified for an inventory of 100 steel SMRFs with varying heights by integrating FEMA P-58 loss-based methodology with the ATC-138 framework. The building information and calculated recovery times were then used in a standard ML pipeline including feature selection, hyperparameter tuning, cross-validation, model evaluation, and model explainability. The results suggest that the ML model can accurately estimate functional recovery using design and geometric features, achieving R2 values of 89% and 93% on the test set for DBE and MCE levels, respectively. In addition, for the studied regular SMRF buildings, the results indicate that building weight and the average strong-column weak-beam ratio are influential design parameters that govern functional recovery time, suggesting that a recovery-oriented design of steel SMRFs may benefit from minimizing building weight and avoiding overt column upsizing.

Infrastructures doi: 10.3390/infrastructures11070212

Authors: Shifu Wang Changxing Zhang Biao Xia Meiqian Wang Zhiyi Tang Wei Xu

Transparent soil technology provides a non-invasive experimental approach for visualizing internal processes in geotechnical infrastructure systems, where soil deformation, seepage, erosion, and failure evolution are often difficult to observe using conventional model tests. This review examines the material systems and applicability of transparent soil with emphasis on infrastructure-related applications, including foundation engineering, underground construction, seepage and grouting, internal erosion, slope failure, disaster mitigation, and thermal monitoring. The discussion focuses on transparent sand and transparent clay, comparing their engineering relevance, typical application scenarios, and main limitations rather than treating transparency as the sole criterion for material selection. Based on the reviewed studies, a four-dimensional applicability framework is proposed, consisting of mechanical similarity, optical measurability, system compatibility, and scenario matching. This framework is used to clarify how transparent soil can support mechanism interpretation, model calibration, and scheme comparison in infrastructure-related geotechnical experiments. The review indicates that transparent soil is particularly useful for revealing displacement fields, flow paths, localized deformation, and progressive failure processes in foundations, tunnels, slopes, and other geotechnical systems. However, direct extrapolation of model test results to engineering design parameters remains constrained by material equivalence, optical measurement conditions, model scale, and similarity calibration. Overall, the proposed framework and synthesis provide a systematic reference for transparent soil material selection, infrastructure-oriented scenario matching, and the assessment of applicability boundaries in transparent soil model tests.

Infrastructures doi: 10.3390/infrastructures11070211

Authors: Jinhong Yu Hongyue Liu Manyu Wang Yingen Shi Xiangmin Yu Jinfeng Xu Jiahao Zhan

Buried parallel pipelines are increasingly common, and unlike single-line systems, adjacent pipelines exhibit mutual interactions. This study investigated their behavior under symmetric and asymmetric conditions, considering empty pipeline, water filling, and normal working, as well as the effects of diameter-to-thickness ratio, spacing, and burial depth. The results indicate that the pipeline–soil interaction differs significantly from single pipelines and is highly dependent on working conditions. Under symmetric conditions, vertical and horizontal deformations differ by 3.0–4.3 mm; contact pressure is nearly circular under empty pipeline and water filling conditions, but elliptical under normal working condition; tangential force follows a cloverleaf pattern; and soil pressure at the pipeline top and vertical soil support lose axial symmetry, with unequal horizontal resistance on either side. Under asymmetric operations, the largest differences occur under the water filling—normal working condition, with the soil pressure at the top of the #1 pipeline being 36.7% lower than that of the #2 pipeline. Moreover, smaller diameter-to-thickness ratios reduce sensitivity to working conditions, while greater burial depth linearly increases deformation and soil pressure, amplifying inter-pipeline differences. Pipeline spacing has only limited effects. These findings reveal the mechanical properties of parallel pipelines under various operating scenarios, providing a reference for the design of multi-pipeline systems.

Infrastructures doi: 10.3390/infrastructures11070210

Authors: Obiri Gyadu-Asiedu Simon Ofori Ametepey Clinton Aigbavboa Hutton Addy Nana Akua Asabea Gyadu-Asiedu

Road infrastructure accounts for a substantial and systematically under-reported fraction of construction-related embodied carbon globally. Despite rapid network expansion across sub-Saharan Africa, no peer-reviewed study identified in the databases searched has established a quantified embodied-carbon baseline for Ghanaian road construction, creating a notable gap in national carbon accounting and low-carbon procurement policy. This study addresses that gap through two integrated components: a PRISMA 2020-guided systematic review of road-LCA and embodied-carbon literature, and a first-pass scenario model for Ghanaian low-volume paved roads (LVRs) bounded at A1–A3 (cradle-to-gate). Database searches of Scopus and Web of Science (14 March 2026) returned 3193 records; following deduplication and two-stage screening, 574 studies were included in the review. A staged harmonisation procedure converted 211 benchmark-shortlisted studies to comparable units, yielding a harmonisation subset of 29 studies and a final benchmark pool of 10 studies expressed as kgCO2e per lane-kilometre (3.5 m lane width). The scenario model applies emission factors from the ICE Database (Educational V4.1, 2025) to three pavement configurations drawn from the Ghana Manual for Low Volume Roads (Parts B and D), all surfaced with double bituminous surface treatment (DBST); Otta seal is evaluated as a sensitivity case. Results show A1–A3 embodied carbon of 14,165 kgCO2e/lane-km for Scenarios S1 and S3 (SC2/TLC 0.01 and SC4/TLC 1.0, respectively) and 12,564 kgCO2e/lane-km for Scenario S2 (SC3/TLC 0.3). Bituminous binder accounts for 30–34% of A1–A3 emissions despite representing less than 1% of pavement mass, identifying binder supply as the primary carbon lever. The two most structurally comparable benchmark studies, chip-seal treatments in the USA, bracket the Ghana values at 12,687–16,400 kgCO2e/lane-km, providing external plausibility validation. To the best of our knowledge, this study delivers a peer-reviewed, reproducible A1–A3 (cradle-to-gate) carbon baseline for Ghanaian LVR construction, a PRISMA-compliant synthesis of road embodied-carbon evidence, and a documented framework for early-stage carbon benchmarking in West African road infrastructure planning.

Infrastructures doi: 10.3390/infrastructures11060209

Authors: Hyogyu Kim Chang-Woo Lee

Road tunnel ventilation systems have traditionally been designed to dilute vehicle-generated pollutants and control smoke during fires. However, the thermal environment, including temperature and humidity, is not the variable taken into consideration. Despite the operation of its ventilation system, Boryeong Subsea Tunnel (6.9 km), the longest subsea road tunnel in Korea, has experienced severe condensation since its opening in December 2021. As hot, humid ambient air enters the tunnel and meets wall surfaces cooled by seawater and the surrounding ground, condensation and fog may form, reducing visibility. To investigate the causes of condensation and develop a decision-making tool for prediction, a variety of tasks were carried out: (1) field measurements of temperature, humidity, tunnel wall temperature, and tunnel air velocity; (2) development of a 1D model for condensation rate quantification; and (3) 3D CFD simulations. Condensation occurred mainly from June to September, with the most severe conditions in July and August. Both the 1D model analysis and the CFD simulations showed good agreement with field measurement data, with wall temperature errors within 7.3%. Under current traffic conditions (with a peak of approximately 250 veh/h), the annual condensation volume was estimated at approximately 12,415 ton/year. Under the design traffic volume (1550 veh/h), heat from vehicles was found to effectively suppress condensation. The Condensation Contour Map (CCM) was developed as a decision support tool to predict the likelihood and amount of condensation based on the tunnel air temperature and humidity conditions. The results of this study clearly indicate that condensation should be explicitly considered in the design and operation of long subsea road tunnels.

Infrastructures doi: 10.3390/infrastructures11060208

Authors: Tianxiao Tang Ke Wang Xin Liu Yunxi Han Lin Wang

Well-point dewatering can rapidly lower the level of groundwater, making the capillary zone fall below the depth at which the subgrade is frozen. This can have the effect of inhibiting frost heave in the subgrade. This paper draws upon a project focused on treatment of the frozen section of the Shenmu–Shuozhou railway subgrade to present a method for calculating the dynamic groundwater level when pumping water using group wells. A dynamic groundwater seepage model is established, and the influence of the type of pumping wells, their layout, and spacing on variations in the groundwater level and the inhibition of frost heave in the subgrade is examined. This forms the basis of an optimal treatment plan for the frozen section of the Shenmu–Shuozhou railway. Simulation results show that a double row of wells along the route that fully penetrate the phreatic aquifer led to a large drop in the groundwater level, thus significantly inhibiting frost heave. Reducing the spacing of the wells enhances the dewatering effect and frost heave inhibition, but also reduces the strength and stability of the subgrade, so the right balance needs to be struck between the stability requirements and the frost-heave inhibition requirements. This research can serve as a reference for the treatment of frost heave in silty subgrades.

Infrastructures doi: 10.3390/infrastructures11060207

Authors: Giulia Del Serrone Giuseppe Loprencipe Laura Moretti

This study presents an AI-enabled framework for automated pavement condition assessment in urban environments by integrating YOLOv8-based distress detection, computational Pavement Condition Index (PCI) estimation, and comparative validation against manual PCI inspections and Pavement Surface Evaluation and Rating (PASER) scores. A YOLOv8 object-detection model, implemented in Python and trained on the publicly available N-RDD2024 dataset, was developed to identify longitudinal cracks, transverse cracks, alligator cracking, and potholes. The model achieved an accuracy of 84.6%, a precision of 89.6%, and a recall of 86.3%, demonstrating robust detection performance under heterogeneous environmental conditions. Dash-cam imagery collected along 6.3 km of urban flexible pavements was processed through an automated workflow that detects pavement distresses, estimates their severity and extent, and computes PCI values according to ASTM D6433-20 procedures. Automated PCI values were compared with manual PCI inspections and PASER ratings generated by the Blyncsy platform across 23 pavement sections. Statistical validation between automated and manual PCI assessments returned an R-squared of 0.925, a Pearson correlation coefficient of 0.962, a Spearman correlation coefficient of 0.955, a Mean Absolute Error of 5.0 PCI points, and a Root Mean Square Error of 6.1 PCI points. Compared with the proposed framework, PASER ratings exhibited lower agreement with manual PCI assessments and generally overestimated the pavement condition. The results demonstrate the potential of low-cost AI-based systems for large-scale pavement monitoring. Nevertheless, performance degradation was observed under challenging environmental conditions and in heavily deteriorated sections, highlighting the need for improved distress quantification, dataset balancing, and multimodal sensing integration.

Infrastructures doi: 10.3390/infrastructures11060206

Authors: Zhengyu Wu Jahanzeb Javed Muhammad Usman Siddiq Muhammad Ahmed Qurashi Ping Lyu

The increasing generation of waste toner from printers and photocopiers presents significant environmental and disposal challenges. This study investigates the feasibility of utilizing waste toner as a modifier in asphalt binder to enhance performance and sustainability. Bitumen with a penetration grade of 60/70 was modified with waste toner at varying contents (0–30%). The modified binders were evaluated using penetration, ductility, and softening-point tests to assess their physical behavior. Results indicate that increasing toner content reduces penetration and ductility while improving the softening point, indicating enhanced temperature resistance. Furthermore, asphalt mixtures were evaluated using both destructive (Marshall stability) and non-destructive testing (ultrasonic pulse velocity) methods to provide a comprehensive performance assessment. In addition, an artificial neural network (ANN) model was developed to predict and evaluate the performance of toner-modified mixtures. The findings demonstrate that waste toner can be effectively used as a sustainable modifier in asphalt mixtures, thereby improving material performance and reducing environmental impact.

Infrastructures doi: 10.3390/infrastructures11060205

Authors: Elsayed Elkamhawy Ashraf Jatwary Basheer M. Nasef Mahmoud T. Ghoniem Hewida Omara Hany F. Abd-Elhamid Martina Zeleňáková Hazem M. Eldeeb

Understanding and accurately predicting the outflow hydrograph from embankment dam breaches is essential for managing the associated flood hazard and improving emergency preparedness. This work simulates the breaching process using a high-resolution 3D computational fluid dynamics (CFD) model, a critical natural hazard for earth-fill dams under overtopping conditions. The model was validated against the experimental data, showing high accuracy in predicting breach development and failure timing. A parametric analysis was performed to assess the influence of the initial breach geometry on erosion dynamics. The results indicated a high sensitivity, while increasing the breach width by 5% led to an average 11% increase in the erosion rate, and decreasing the depth by 5% caused an average 16.5% rise. To enhance predictive capabilities for this hazard, a multilayer neural network (MLNN) was trained on the CFD-generated dataset. The network utilized breach geometry and time as inputs to forecast the peak outflow and erosion rate, achieving excellent accuracy (RMSE = 0.019, R2 = 0.99). This integrated modeling strategy combines data-driven learning with physics-based simulation and demonstrates its effectiveness for laboratory-scale dam breach modeling. This approach is a step toward more efficient surrogate-based tools for flood risk analysis, though its extension to full-scale dams and varied material properties requires additional validation and scaling analyses beyond the scope of this work.

Infrastructures doi: 10.3390/infrastructures11060204

Authors: Furkan Luleci Sadig Nuraliyev

This study investigates the feasibility, accuracy, and data-sufficiency requirements of smartphone- and smartwatch-based crowdsensing for pedestrian bridge modal identification under real-world conditions. Full-scale experiments were conducted on a bridge across two crowdsensing scenarios with varying dynamic excitation intensities by six pedestrians performing walking, running, and bicycling activities while carrying smartphones and wearing smartwatches. Triaxial acceleration data were collected over 300 s and processed using a framework comprising preprocessing, modal estimation, growing-window convergence analysis, and block-bootstrap uncertainty quantification. Using the full dataset, both devices reliably identified the four consistently detectable bridge modes with average errors of approximately 3% across the scenarios relative to the benchmark. In the convergence analysis, smartwatches consistently produced narrower confidence intervals and more stable early-window estimates, which may be related to their more constrained wearing condition and reduced incidental motion compared to pocket-carried smartphones. Higher pedestrian excitation with additional pedestrians running accelerated the convergence, reducing the required data duration and number of pedestrian passes, albeit with increased uncertainty. The study established data-sufficiency thresholds, showing that reliable modal estimates require in the range of 5–17 walking or running passes, while bicycling passes range from 14 to 28, depending on bridge excitation level and device type. Results demonstrate that commodity smartphones and smartwatches are viable, scalable, and cost-effective platforms for crowdsensed bridge modal identification, provided that uncertainty ranges are properly accounted for and sufficient passes across different pedestrian activities are collected to achieve the desired accuracy.

Infrastructures doi: 10.3390/infrastructures11060203

Authors: Xifan Chen Li Zhang Xu Tang

This study aims to develop a decision-making framework for equalizing urban electric vehicle (EV) charging services, which is applied to improve Wuhan’s charging infrastructure. Using grid units as the basic analytical units, the study constructs measurement models for two scenarios—daily commuting and weekend travel—including a spatial demand index based on classified population distribution prediction, a spatial supply index derived from regional charging station statistics, and a supply–demand balance index. Grading systems are established for each scenario’s demand, layout thresholds, and supply, together with an integrated classification combining both scenarios. According to the suitability of grid units for service improvement, three optimization strategies are proposed: adding charging stations, expanding existing stations, and retrofitting parking lots. Evaluation methods are designed to assess spatial equilibrium pre- and post-optimization for residential quarters and commercial POIs. An empirical case study of Wuhan’s main urban area shows that service satisfaction reaches 88.68% for residential quarters and 75.93% for commercial POIs under the current conditions. The proposed scheme recommends the addition of 6 new stations, expansion of 23 stations, and retrofit of 52 parking lots, increasing satisfaction to 99.16% and 89.66%, respectively. The model provides a feasible technical framework for urban EV charging station planning.

Infrastructures doi: 10.3390/infrastructures11060202

Authors: Binbin Liu Mingming Wang Xiaolei Zhu Wanbo Zhang

Crack opening and reinforcement stress are two complementary indicators of the service state of reinforced concrete hydraulic structures, yet they are often predicted separately. This study develops a data-driven multi-task temporal fusion framework for joint 48 h ahead prediction of dam crack responses and rebar stress using multi-source monitoring data. The measured data comprise five crack-monitoring series, five rebar stress series, local temperature channels, reservoir water level, antecedent rainfall, and an auxiliary environmental signal over approximately four years. Target responses are aligned only at common measured timestamps; no synthetic target observations are introduced. A simplified engineering layout and plan-based crack–rebar distances are further used to examine whether an explicit spatial prior can strengthen the shared temporal representation without introducing synthetic target values. A residual multi-task temporal fusion network (MTTF-Net) is proposed with a shared Transformer encoder, attention pooling, task-specific decoders, and a response-continuity regularization term. The model is compared with persistence, Ridge regression, random forest, Extra Trees, XGBoost, and GRU baselines under a chronological train/validation/test split. For the independent test period, Ridge regression obtains the lowest overall RMSE (2.2968), whereas MTTF-Net provides the lowest crack RMSE (0.0141), the lowest overall MAE (1.0035), and the second-best overall RMSE (2.3813). Distance-informed ablation, denoted as MTTF-Net-S, remains close to MTTF-Net in macro-averaged R2 but is not superior in the overall test metrics, indicating that the available horizontal distances are valuable engineering metadata but cannot replace richer three-dimensional structural connectivity. These results indicate that the monitoring data contain a strong linear autoregressive component, while multi-task temporal fusion improves nonlinear crack response prediction and remains competitive for stress forecasting. The source code is prepared as a public implementation package, whereas the measured monitoring dataset is subject to data owner restrictions.

Infrastructures doi: 10.3390/infrastructures11060201

Authors: Liqiong Li Changjie Yao Mingtang Chai Shuhong Wang

Pavement damages along the Qinghai–Xizang Highway (QXH) in permafrost regions are jointly controlled by geographical and engineering factors, leading to higher damage rates than in non-permafrost regions. However, the overall development trend of these damages and the spatial–temporal patterns have not been systematically quantified. To analyze the spatial distribution of different pavement damages, reveal the spatial–temporal associations, and analyze the spatial heterogeneity of the driving factors, three field surveys were conducted in 2014, 2019 and 2024, with records of seven major pavement damages. Statistical analyses were used to examine the relationships among single and co-occurring damages. Then, a novel geographical model, combining a graph convolutional network with multi-scale geographically weighted regression (GCN-MGWR), was further developed to treat the QXH as a linear geographic unit and to assess the spatial heterogeneity and relative contribution of different influencing factors. The results show that the mean pavement damage ratios in permafrost regions during the three surveys are 4.21%, 6.82%, and 4.74%, respectively, with crack-type damages (transverse, longitudinal, and block cracking) exhibiting the highest occurrence rates. The three strongest pairs of correlations are transverse and longitudinal cracking (0.584), transverse and block cracking (0.570), and waving and rutting (0.622). The primary factors influencing crack-type damages are embankment thickness, mean annual ground surface temperature (MAGST), elevation and existing damages. Transverse and longitudinal cracking show a pronounced increase with rising MAGST, and embankment thickness below 1 m or above 4 m significantly contribute to the development of both crack types (SHAP > 0.5). Overall, the evolution of crack-type damages has shifted from being primarily controlled by geographical factors to being controlled by the combined influence of engineering and geographical factors during 2014–2024. The factor contributions identified by the GCN-MGWR model provide quantitative support for the regional adaptive design and specific maintenance of roadway in permafrost regions.

Infrastructures doi: 10.3390/infrastructures11060200

Authors: Berkin Uluutku Korkut Kaynardag Daisuke Oshima John Cotter Fikret Necati Catbas

Connected vehicle platforms enable large-scale collection of vehicle dynamics data from production fleets, creating opportunities for passive roadway monitoring using onboard sensing systems. While existing vibration-based approaches primarily focus on pavement roughness estimation, the ability of fused onboard signals to capture defect-level characteristics remains insufficiently explored. This study investigates whether Road Surface Monitoring (RSM) signals, developed by Honda as an integrated OEM sensing approach, contain distinguishable patterns associated with specific road surface defects. A framework is developed to analyze, detect, and classify defect-related vibration signatures using these fused signals. The approach introduces the Defect Consistency Index (DCI), which measured a 29% average difference between pothole and patching signal signatures within the dataset. A threshold-based Defect Identification Algorithm (DIA) was then applied to detect defective segments, achieving 89% detection accuracy. A machine learning pipeline using shape-based features was subsequently used to classify potholes and patching, achieving up to 90% classification accuracy on the evaluated dataset. The framework was evaluated using real-world RSM data collected from a single instrumented vehicle within a limited geographic region. The results indicate that fused vibration signals contain recurring defect-related patterns that may support defect-level analysis using compact, non-visual measurements. These findings indicate the potential of connected vehicle vibration sensing for scalable roadway monitoring while highlighting the need for broader validation across vehicles, environments, and defect conditions.

Infrastructures doi: 10.3390/infrastructures11060199

Authors: Sepehr Mohammadi Dongzhao Jin Meng Wu Zhongda Liu Zhanping You

The incorporation of crumb rubber derived from waste tires in asphalt pavements has gained increasing attention as a strategy to enhance performance while reducing environmental impacts, particularly in cold regions such as the Midwestern United States, where pavements are subjected to severe thermal stresses and freeze–thaw cycles. Despite the numerous performance benefits observed in previous laboratory-scale studies, field demonstrations can play a critical role in validating the use of recycled waste tires as asphalt additives. This study examines the performance benefits and environmental impacts of incorporating recycled tire rubber into asphalt mixtures via a dry modification process for cold-climate applications. Building on these findings, this paper is based on a full-scale field demonstration of a dry-process rubber-modified asphalt pavement constructed in Ann Arbor, Michigan. Performance testing was conducted at both the binder and mixture levels, and field cores were collected during the construction of field sections. To complement the performance evaluation, a life-cycle assessment (LCA) was conducted to quantify the environmental impacts of rubber-modified asphalt and conventional asphalt. The results indicate that successful rubber incorporation, combined with improved low-, intermediate-, and high-temperature performance, enhances long-term durability compared with control sections. Moreover, despite slightly higher initial environmental impacts associated with rubber incorporation, improved durability and reduced maintenance frequency can lead to lower life-cycle impacts over the long term. The findings highlight the potential of rubber-modified asphalt as a sustainable, resilient solution for cold-region pavements, offering practical insights for agencies seeking to balance performance and environmental impacts in future infrastructure design.

Infrastructures doi: 10.3390/infrastructures11060198

Authors: David López-García Carlos Alonso-Troyano David Llopis-Castelló

The need for potentially more sustainable road rehabilitation solutions has driven the use of reclaimed asphalt pavement (RAP) in bituminous mixtures. However, high-RAP content remains a technical challenge due to binder ageing, which increases mixture stiffness and adversely affects its mechanical performance. The aim of this research is to evaluate three strategies for correcting aged binder in asphalt concrete (AC) 16 surf S mixtures containing 50% RAP: rejuvenator, regenerator, and softer virgin bitumen. To this end, four asphalt mixtures were evaluated through tests on air void content, water sensitivity, resistance to permanent deformation, and stiffness modulus, in accordance with European standards. The results show that the reference mixture without binder correction exhibits excessive stiffness, whereas the mixture incorporating a rejuvenator showed the most favorable combination of the mechanical indicators evaluated, combining a significant reduction in stiffness modulus with high water resistance and adequate rutting resistance. The mixture with regenerator showed an intermediate response, while the exclusive use of a softer bitumen did not achieve satisfactory overall performance. The results confirm that the use of high-RAP contents in AC 16 surf S mixtures can be feasible, provided that an appropriate strategy for rheological correction of the aged binder is applied.

Infrastructures doi: 10.3390/infrastructures11060197

Authors: Carlo Rainieri Santiago Gómez Molina Ilenia Rosati Alessio De Corso

Vibration-based Structural Health Monitoring (SHM) exploits automated Operational Modal Analysis (OMA) to track changes in modal parameters over time for subsequent statistical pattern recognition and anomaly detection. However, weak excitation, measurement noise, non-stationarities, non-linearities, and model inaccuracies can jeopardize the reliability of automated OMA and pollute the modal parameter time series with a number of outliers or spurious estimates. These have an impact on statistical pattern recognition and consequently, the anomaly detection accuracy. Thus, a preliminary data cleansing to enhance the robustness of modal parameter tracking is imperative to ensure the reliability of SHM outcomes. Clustering techniques represent an attractive solution to automatically identify underlying data patterns and discriminate possible spurious results. However, the curse of dimensionality is often an issue in the application of such techniques to time series of experimentally identified modal parameters. To mitigate this issue and, at the same time, the computational efforts, the present study proposes an innovative approach leveraging clustering techniques coupled with mode-pairing constraints for robust and automatic tracking of modal parameters in the context of vibration-based SHM applications. Different clustering algorithms have been embedded in the proposed data processing strategy and applied to a real dataset collected on a full-scale structure under operational conditions. The comparative performance assessment demonstrated how DBSCAN outperforms other clustering methods in the context of the proposed approach, allowing the effective separation of the physical poles from the spurious ones even in the presence of closely spaced modes and highly polluted feature space.

Infrastructures doi: 10.3390/infrastructures11060196

Authors: Sudip Basack Meshel Q. Altahtani Saiful Islam Moses Karakouzian

Large civil infrastructures, including high-rise buildings, bridges, offshore platforms, transmission towers, tall chimneys, basements below the water table, etc., are often supported on pile foundations. Apart from the usual dead loads and live loads imposed by superstructures, these piles are often subjected to significant uplift forces due to overturning moments or hydrostatic pressure resulting from the effects of wind and wave loading, traffic movement, buoyancy, etc. Piles that withstand tensile loads are termed tension piles. Since the soil is unable to resist tensile stress, the pullout loads imposed on tension piles are prevented primarily by downward skin friction at the pile–soil interface, as well as by the self-weight of the piles. In this paper, a numerical model was developed using boundary element analysis, wherein piles were assumed to be made of an elastic–perfectly plastic material, and the soil was modeled using a parabolic model. The developed model was validated with available experimental results, and acceptable agreement was found. An in-depth study by detailed parametric analysis revealed that the parabolic soil model yielded satisfactory results. Extensive full-scale case studies were also performed to study the influence of various factors on tension pile performance. A set of important conclusions was drawn from the entire work.

Infrastructures doi: 10.3390/infrastructures11060195

Authors: Laura Valentina Hernández García Irene Méndez-Manjón Eva Martínez López Pedro Plasencia-Lozano

This study presents a replicable diagnostic framework for analysing latent safety vulnerability at complex junctions by integrating UAV-based observation, trajectory extraction, movement-level operational performance modelling, and regulatory benchmarking. Grounded in Vision Zero/Safe System principles, the approach is demonstrated at Junction 50 of the A-66 motorway in Mieres (Spain), a constrained urban interchange where motorway access/egress overlaps with local cross-town movements. Two one-hour UAV datasets were collected during peak periods and processed with GoodVision to extract trajectories, turning-movement counts, origin–destination patterns and recurrent irregular manoeuvres. These outputs were combined with SIDRA INTERSECTION indicators (e.g., LOS, delay and capacity utilisation) and assessed against the Spanish geometric design standard to identify design–operation misalignment, including a targeted 3D sight-distance check at the stop-controlled exit. The results show systematic behavioural adaptations at critical decision points, including informal side-by-side queuing at nominally single-lane exits and queue bypassing via adjacent auxiliary areas, co-occurring with capacity-strained movements (LOS E–F) and normative inconsistencies in lane type/length and channelisation. The framework translates high-resolution behavioural evidence into intervention-relevant outputs (critical movements, concentration zones and explicit design–behaviour mismatches) without relying on crash frequency as the primary signal, supporting proactive prioritisation in constrained legacy layouts.

Infrastructures doi: 10.3390/infrastructures11060194

Authors: Hawraa F. Jabbar Miami M. Hilal Mohammed Y. Fattah Karim Sherif Mostafa Norbaya Sidek Mohamed A. Hafez

This study investigates asphalt mixture workability using a high-capacity torque-based device under semi-industrial laboratory conditions. Unlike conventional laboratory-scale mixers, the proposed system accommodates batch sizes up to 15 kg, enabling more realistic simulation of plant and field mixing conditions. Torque response was monitored during the mixing of conventional, warm-mix, and RAP-containing asphalt mixtures. Predictive models were developed using stepwise regression to relate torque to mixture parameters, including temperature, RAP content, and binder type. Results indicate that RAP significantly increases mixing torque, while elevated temperatures reduce resistance to mixing. Although the developed models demonstrated moderate to good explanatory power (R2 = 0.63–0.77), they provide useful comparative insights into asphalt mixture workability rather than absolute predictions. The proposed torque-based methodology offers a practical framework for workability assessment and quality control of asphalt mixtures beyond traditional laboratory scales.

Infrastructures doi: 10.3390/infrastructures11060193

Authors: Yingchun Yin Wengui Zhang Wei Wan Yile Chen Zunqing Liu

To investigate the rheological properties and compositional changes in SBS-modified bitumen under different aging conditions in the unique environmental conditions of the Xinjiang region, this study selected a local 70# base bitumen from Xinjiang and prepared modified bitumen by adding 4.0%, 4.5%, and 5.0% SBS modifier, respectively. RTFOT and PAV were used to simulate the short-term thermal-oxidative aging and long-term oxidative pressure aging processes of the bitumen samples, respectively. The three key indicators and dynamic rheological properties of the bitumen were tested for the original sample, as well as before and after short-term thermal-oxidative aging and long-term oxidative pressure aging. Thin-layer chromatography/flame ionization detection (TLC/FID) was used to analyze the migration patterns of the samples’ chemical components, and a random forest model was employed to establish a quantitative mapping between the four components of the modified bitumen and the rutting factor over a wide temperature range. The results indicate that aging weakens the improvement effect of SBS on the high-temperature performance of bitumen. However, 4.5% SBS-modified bitumen subjected to long-term oxidative pressure aging still maintains the best high- and low-temperature performance, elastic recovery capacity, and fatigue resistance compared to other dosage levels. It also has the highest bitumen content, which verifies the high-temperature performance of this dosage at the component level. Therefore, the optimal SBS dosage is recommended to be 4.5%. Notably, as the SBS content increases, it significantly regulates the increase in heavy fraction content during the aging process, while the decrease in light fraction content is not significantly affected by the content. Based on the random forest algorithm, a mapping relationship between fractions and properties under fully aged conditions was established. This study provides a theoretical basis for research on the modification and aging mechanisms of Xinjiang bitumen.

Infrastructures doi: 10.3390/infrastructures11060192

Authors: Anna M. Rakoczy Joanna Szczech Jan Winkler

Regulations in Poland require acceptance load tests to verify bridge response under moving loads before structures are approved for operation. These tests are mandatory for new bridges, after major renovations, and for reconstructed structures, and may also be conducted as supplementary assessments of existing bridges to determine their load-carrying capacity. This paper presents one of the first documented applications, to the authors’ knowledge, of low-cost sensing technology for capturing bridge displacements with sub-millimeter tracking precision during acceptance load testing. The study explores the use of modern remote sensing methods based on digital image correlation (DIC) to assess vertical displacements of a truss railway bridge span under moving loads. Video data were recorded using a standard smartphone under nighttime conditions with artificial lighting, demonstrating a highly accessible and cost-effective measurement approach. The collected data were processed using the DES Vision System and compared with results obtained from traditional measurement techniques, such as accelerometers, enabling an evaluation of the accuracy and precision of the DIC method. The findings show that smartphone-based video recordings can provide displacement measurements with millimeter- to sub-millimeter-level tracking precision. Additionally, a numerical finite element method (FEM) model was developed to support interpretation of the structural response under moving loads.

Infrastructures doi: 10.3390/infrastructures11060191

Authors: Adiya Zhumagulova Rauan Lukpanov Duman Dyussembinov Mariya Smagulova Galiya Asanova Manarbek Zhumamuratov Andrey Chzhen Daniyar Zakirzhan

This article presents the results of tests evaluating the physical and mechanical properties of a modified hydraulic concrete formulation based on Portland cement, intended for use in general construction. The additive consists of post-alcohol distiller’s grains (PaB), soapstock (Sp), caustic soda (NaOH), granite dust (Gr) and acrylic latex (Lx). These components contribute to transforming the strength characteristics of concrete in compression and bending, as well as its water absorption, water permeability and chemical resistance. Based on the results obtained, the effectiveness of the additive was assessed, as was the quantitative improvement in concrete properties, including an evaluation of the life cycle of reinforced concrete structures in aggressive environments. According to the research results, an optimal composition was obtained which increases compressive strength by 6.2%, flexural strength by 7.9%, decreases water absorption by 50.1%, decreases the filtration coefficient by 97.4%, and increases chemical resistance by 42.8%.

Infrastructures doi: 10.3390/infrastructures11060190

Authors: Leila Mirzaei Clifford B. Fedler Tewodros Ghebrab

Agricultural biomass ashes are increasingly used as sustainable supplementary cementitious materials (SCMs) to reduce cement-related carbon emissions and improve concrete performance. However, their effects on compressive strength depend on the SCM type, replacement level, and physical and chemical properties. These variables are often overlooked in machine learning studies focused on single SCM types and absolute strength prediction, limiting transferability across heterogeneous SCM datasets. This study develops an interpretable machine learning framework using a compiled dataset covering 18 agricultural biomass ash SCMs (bio-SCMs) used in concrete. Input features include concrete mixture proportions, the SCM replacement level, chemical composition, and specific surface area (SSA), while the target variable is the 28-day compressive-strength ratio relative to the companion control mixture. Among the five evaluated models, XGBoost achieved the best performance, with weighted 10-fold cross-validation R2 values around 0.80. SHapley Additive exPlanations (SHAP) results were interpreted as model associations rather than causal mechanisms. Higher SCM SiO2 content, pozzolanic oxide content, superplasticizer dosage, and baseline control mixture strength were associated with more favorable strength ratios; SCM SSA showed a mild positive tendency, whereas a higher SCM replacement level, water-to-binder ratio, and loss on ignition were associated with less favorable strength ratios. SCM-specific response analysis further identified literature-derived screening ranges based on observed and interpolated replacement levels rather than machine learning extrapolation.

Infrastructures doi: 10.3390/infrastructures11060189

Authors: Domenic Graffi Hendrik Morgenstern Katharina Klemt-Albert

Analog, paper-based workflows remain the norm in the condition assessment of reinforced concrete infrastructure, limiting the efficiency with which diagnostic data can be used in maintenance planning. In this research, a pre-processing procedure and BIM implementation workflow for half-cell potential measurement data are proposed, implemented in open-source software, to enable data-efficient integration of diagnostic information into a BIM model. By interpreting the potential mappings as point clouds, areas relevant for maintenance planning are automatically identified through the analysis of geometric features. These identified areas are subsequently transformed into BIM objects that carry the relevant diagnostic information. The results demonstrate that the pre-processing procedure and BIM implementation workflow reduce the number of required BIM objects by over 98% (from 27,040 to 434 elements) and the IFC file size by 97% (from 77.8 MB to 2.3 MB), enabling a lightweight BIM implementation with substantially improved rendering performance.

Infrastructures doi: 10.3390/infrastructures11060188

Authors: Grigório Neto Jorge Pais Simona Fontul Francisco Fernandes

Ground-penetrating radar is widely used in non-destructive pavement evaluation, but the occurrence of multiple hyperbolic signatures in radargrams of cracked pavements remains insufficiently characterized, particularly for top-down and bottom-up cracking. This study investigates the occurrence of detectable hyperbolas in numerical GPR radargrams by comparing two crack models under a controlled two-dimensional numerical design. Model A represents top-down cracking, and Model B represents bottom-up cracking. For each model, four parametric studies were performed by varying crack width, crack depth, asphalt-layer thickness, and granular-layer thickness, yielding 32 simulations in total. All cases were modeled in gprMax2D at 2300 MHz and processed in MATLAB through radargram pre-processing, central A-scan candidate detection, lateral tracking of hyperbolic events, and final classification based on stable retained trajectories. Model A was predominantly characterized by 3H responses, whereas Model B was predominantly characterized by 2H responses, with no 3H case observed. In Model A, crack-width increase was associated with the strongest occurrence change, whereas in Model B, greater asphalt-layer thickness was associated with a reduction from 2H to 1H. The first apex TWT provided a complementary discriminator between the two models. These findings provide controlled numerical reference trends that may support the interpretation of hyperbola occurrence in GPR-based pavement crack assessment.

Infrastructures doi: 10.3390/infrastructures11060187

Authors: Zhanjing Wu Zhong Tao Longfei Zhang Zhengjia Wu Qiang Huang Haisu Sun

To investigate the influence of isolation-layer eccentricity on the torsional response of step-terrace mountain (STM) structures, a 1:10 scaled reinforced concrete model was designed and tested using shaking table experiments. Both isolated and non-isolated configurations were considered, and different eccentricity levels were achieved by adjusting the bearing layouts in the upper and lower isolation layers. The torsional response was evaluated in terms of torsional angle, torsional displacement ratio, and relative torsional effect. The results indicate that the non-isolated STM structure exhibits pronounced torsional amplification and progressive damage accumulation. Deformation and damage are concentrated in the upper stories and dropped-story region, eventually leading to a stiffness–degradation–dominated failure pattern. In contrast, the STM isolated structure effectively suppresses torsional response, and inter-story rotations remain small and relatively uniform along the height, indicating that seismic deformation is primarily redistributed within the isolation layers rather than amplified in the superstructure. The experimental results further demonstrate that torsional behavior is governed by the coupling effect between isolation-layer eccentricity and seismic input direction. The eccentricity in the upper isolation layer plays the dominant role in triggering torsional amplification, while simultaneous eccentricities in both isolation layers produce a cumulative torsional effect. When the eccentricity of the isolation layers is controlled within 5%, the torsional displacement ratio remains below 1.2, while the non-isolated structure reaches values exceeding the code limit of 1.5. In addition, slope-direction excitation intensifies absolute torsional deformation due to overturning effects induced by elevation differences. These findings highlight that torsional response in STM isolated systems is controlled by the interaction between vertical irregularity and isolation-system asymmetry.

Infrastructures doi: 10.3390/infrastructures11060186

Authors: Abbas F. Jasim Rana A. Yousif Sady A. Tayh Safaa A. Mohamad Teba T. Khaled

The pavement surface temperatures in Iraq are remarkably high, causing the asphalt to deteriorate quickly, shortening its service life. While a large amount of corn husk, an agricultural waste, is available for use as an asphalt modifier, researchers have not yet fully investigated this option. In this study, the use of corn husk fiber powder (CHFP) as a long-term modifier for asphalt binders and mixtures that are exposed to high-temperature conditions is evaluated. CHFP was mixed into a 40–50 penetration grade asphalt binder at concentrations ranging from 0.0% to 0.6% by weight. Performance was assessed using laboratory tests such as penetration, softening point, rotating viscosity, dynamic shear rheometer (DSR), aging (RTFOT and PAV), and wheel tracking. The findings revealed that CHFP greatly lowers penetration while increasing the softening point, indicating increased stiffness and high-temperature stability. Rheological research showed an increase in the rutting parameter (G*/sinδ) and viscosity, as well as reduced temperature susceptibility. At the mixed level, CHFP reduced rut depth while improving dynamic stability, indicating increased resistance to permanent deformation. The best performance was obtained at 0.3% CHFP, after which, improvements decreased due to probable dispersion constraints. The performance improvement is related to the creation of a reinforcing fiber network and the absorption of light asphalt components. Overall, CHFP is a promising, environmentally friendly and cost-effective addition for increasing asphalt pavement performance and promoting sustainable waste management.

Infrastructures doi: 10.3390/infrastructures11060185

Authors: Xing-Ze Duan Zhao-Jun Liu Shuai-Qi Wang Rui-Jie Xia Wei Li Ju Liu Guo-Hua Song Zhi-Xiao Shi Jun Shi Ao Yang Kuang-Yu Dai

To balance ultra-early strength development and workable time in cement-based grouting materials for rapid repair applications, an ultra-early high-strength grout system was developed by regulating the dosage of an accelerating agent (CF), retarder content, and water-to-binder ratio (w/b). The effects of these parameters on setting behavior, workability, mechanical properties, volumetric stability, and durability were systematically investigated. X-ray diffraction (XRD) and scanning electron microscopy coupled with energy-dispersive spectroscopy (SEM/EDS) were further conducted to qualitatively evaluate the hydration characteristics and microstructural evolution of the optimized system. The results showed that CF accelerated early hydration and promoted the rapid formation of ettringite (AFt), which contributed to the development of ultra-early strength. The incorporation of a retarder effectively prolonged the workable time and improved slurry workability. Increasing the w/b ratio enhanced flowability and toughness, although excessive w/b reduced compressive strength. The optimal mixture contained 30% CF, 0.02% retarder, and a w/b ratio of 0.19. Under this condition, the grout exhibited a flowability of 312 mm and compressive strengths of 81.4 MPa at 1 h and 121.3 MPa at 28 d. In addition, low air shrinkage (0.027% at 28 d) and excellent chloride penetration resistance (12 C at 28 d) were achieved. Microstructural observations suggested that the dense structure formed by AFt and C–S–H gel contributed to the improved macroscopic performance. This study provides an engineering-oriented reference for the mix design and performance optimization of ultra-early high-strength cement-based grouting materials for rapid repair applications.

Infrastructures doi: 10.3390/infrastructures11060184

Authors: Wencan Huang Wei Huang Wenjia Huang Qingxiang Zhao Lingyu Zhong Wendi Deng Yufei Wang Qianqian Dong Jianxiong Liao Cai Min

The large-scale accumulation of coal gangue has created increasing environmental pressure, while its use as aggregate in cementitious materials remains limited by its high water absorption, porous structure and unstable mechanical performance. This study develops a machine learning-assisted multi-objective optimization framework for lightweight shotcrete incorporating surface-pretreated coal gangue aggregates and polyvinyl alcohol fibres. Two pretreatment methods—namely, silica-fume slurry coating (CGACM) and dry adsorption activation (CGACD)—were applied to improve the aggregate surface characteristics. Experimental data on compressive strength, splitting strength and density were used to train backpropagation neural networks and support vector machine and random forest models, with hyperparameters optimized by the Beetle Antennae Search algorithm. The trained models were then coupled with a multi-objective optimization procedure to balance mechanical performance, density, material cost and CO2 emissions. The results show that surface pretreatment can improve the performance of coal gangue lightweight shotcrete, while the proposed optimization framework can identify mixture designs with balanced strength, reduced density and improved economic and environmental performance. Compared with untreated or non-optimized mixtures, the optimized surface-pretreated mixtures achieved a more favorable trade-off among mechanical, cost and carbon-emission objectives. This study provides a data-driven approach for the sustainable design and practical utilization of coal gangue in lightweight shotcrete.

Infrastructures doi: 10.3390/infrastructures11060183

Authors: Rui Huang Yige Chen Lanjing Wang Jing Zhan Yuanfan Ji Tingyu Huang Yanbo Yang

This study aims to explore a leakage-aware and explainable machine learning framework for predicting tunnel water inrush quantity (WIQ) under small-sample and high-heterogeneity geological conditions. A project-level dataset was compiled at a fixed spatial granularity of 30 m per excavation segment by integrating forward prospecting outputs, construction-face observations, and geological reports, and six hydrogeological–structural indicators were used to predict the water inflow rate in cubic meters per hour. To overcome data scarcity and improve generalization, a tabular generative adversarial network (GAN) was introduced to augment the training distribution while preserving marginal statistics and inter-variable dependence, and a swarm-intelligence optimizer was employed to tune a Categorical Boosting (CatBoost) regressor for stable performance. In addition, six mainstream tree-based learners were benchmarked under a unified protocol, and model transparency was ensured through a multi-level interpretability suite combining SHapley Additive exPlanations (SHAP) attribution, partial dependence with individual conditional expectation (ICE) diagnostics, and interaction surfaces. Results show that, under the present fixed split, training-set augmentation was associated with improved performance for the evaluated baseline learners, and the proposed hybrid model achieved encouraging hold-out accuracy. However, because the dataset contains only 55 real samples and the test set contains only 11 real samples, the reported performance should be interpreted as an initial project-specific indication rather than robust evidence of generalizable reliability. Interpretability analyses further identify lithologic and reflector-related factors as dominant drivers, and reveal nonlinear response patterns and interaction-sensitive high-risk regions. Overall, the proposed framework shows potential to improve predictive performance and engineering interpretability for the studied project, and may provide a useful reference for drainage and reinforcement planning. Further confirmation through repeated data splitting, additional samples, and external validation is still needed before broader application.

Infrastructures doi: 10.3390/infrastructures11060182

Authors: Xianlin Feng Rui Huang Lin Xu Yi Li Xinyi Liu Feixiang Zeng Zhu Wang

This study investigates upper-reservoir dam-axis siting in pumped-storage hydropower projects, where cut–fill balance and construction cost are critical under complex terrain conditions. Existing approaches still rely heavily on manual interpretation or static GIS-based analysis and therefore do not adequately optimize dam-axis geometry or earthwork balance. To address this limitation, we propose an Improved Adaptive Variable Neighborhood Search (IAVNS) algorithm that integrates high-resolution digital elevation model (DEM) data within a two-layer adaptive framework. The inner layer performs staged planar and elevation adjustments through adaptive neighborhood operators, whereas the outer layer conducts fitness-guided subregion migration to strengthen global exploration. Experiments on the Qiannan pumped-storage project show that IAVNS obtains layouts with improved cut–fill balance. In the 30-run benchmark comparison, IAVNS achieved a mean CFR of 1.31, which is close to, although slightly above, the upper bound of the adopted earthwork-balance reference interval. In the separate 20-run case-study analysis, the average storage-volume deviation was 0.13%, with run-level deviations ranging from −1.39% to 1.16%. In benchmark comparisons, IAVNS improves solution quality by 22.8% relative to the Genetic Algorithm (GA) and by 16.5% relative to classical Variable Neighborhood Search (VNS), while reducing convergence time by 49.5% and 27.4%, respectively. Sensitivity analysis further suggests that the framework remains locally robust under practically reasonable parameter perturbations, and the module-level ablation study indicates that the observed performance gains arise mainly from the problem-tailored search mechanisms for dam-axis siting rather than from a generic combination of metaheuristic components. Taken together, the case-study results, repeated-run comparison, sensitivity analysis, and ablation study support the use of IAVNS as a geometry-oriented decision-support framework for preliminary dam-axis design in terrain-sensitive hydraulic engineering applications.

Infrastructures doi: 10.3390/infrastructures11060181

Authors: Masoud Khanmohamadi Marco Guerrieri

This perspective paper develops a system-level characterization of the transportation metaverse as a persistent, policy-aware digital environment integrating digital twins, real-time data, advanced analytics, and human–machine interaction into a unified operational framework. The study presents a cross-modal review of metaverse applications in road, rail, maritime, and aviation systems, identifying common opportunities, limitations, and research challenges. It further proposes a structured metaverse-based framework for smart roads as a reference case. The framework demonstrates how persistent virtualization, parallel future scenarios, embedded governance constraints, and human-in-the-loop decision support can improve uncertainty-aware planning, management, and operations. The paper positions the metaverse not as a deployable technology, but as an emerging paradigm for transportation governance. The study provides an architectural vision and research agenda for developing more resilient, transparent, and adaptive transportation systems. Potential applications include smart road management, multimodal traffic coordination, real-time operational control, infrastructure resilience planning, and decision support for policymakers under uncertain conditions.

Infrastructures doi: 10.3390/infrastructures11050180

Authors: Peter Koteš Ondrej Krídla Martin Vavruš František Bahleda Michal Zahuranec Jozef Prokop Matúš Farbák

Strengthening existing structures and bridges allows us to continue using them, increase their reliability, resistance, durability and extend their service life instead of demolishing them and replacing them with new ones. This helps to reduce CO2 (decarbonization). The use of prestressed CFRP strips represents the use of new modern materials and new technology for strengthening existing bridges. The paper is focused on the use of prestressed CFRP strips for strengthening a concrete bridge made of precast prestressed box girders as the most suitable strengthening alternative in a given case. This is a technology that is more commonly used for strengthening structures, but it is not common to use this technology for strengthening bridges. There are relatively few examples of using this technology for strengthening bridges, also because these are dynamically loaded structures. The paper firstly presents the diagnostics and calculation of the load-carrying capacity of the railway bridge on a narrow-gauge railway line in Štrbské Pleso, Slovakia, and then the strengthening of the given bridge. The bridge is located in the mountains of the High Tatras in the northern part of Slovakia and bypasses two local roads. The bridge was made from the precast prestressed post-tensioned box girders of six single spans. The visual inspection, diagnostics, and verification of real dimensions and material characteristics were requested. The non-destructive and semi-destructive methods of testing were used to determine the geometrical and materials’ properties. After that, the calculation of the load-carrying capacity was done. For this purpose, a numerical 3D FEM model was created. For determining the load-carrying capacity, the standard approach, given in Eurocodes, was used according to provisions, which take into account the modified (lower) reliability levels and their adequate partial safety factors. From the calculation, it follows that the bridge should be strengthened. The strengthening of the superstructure was done using prestressed CFRP strips in the lower part of the box girders. This is one of the first applications of this modern method of strengthening, not only in Slovakia but in Central Europe as well.

Infrastructures doi: 10.3390/infrastructures11050179

Authors: Ivica Stančerić Saša Ahac Šime Bezina Tamara Džambas

The roundabout design procedures specified in current standards and guidelines presuppose that the centrelines of the approach legs intersect at right angles at the geometric centre of the roundabout. In urban areas, this requirement cannot always be met due to fixed structural constraints along the approaches. Here, a lateral or radial offset of the approach legs from the roundabout’s geometric centre is required. This offset plays an important role in roundabout design, as it affects the roundabout’s ability to control vehicle speed. This study investigates the effects of a lateral approach leg offset on the geometric design of urban single-lane roundabouts and the driving speeds through them. Accordingly, speed analyses were conducted for numerous theoretical roundabouts with outer radii between 15 and 25 m, designed based on swept path analysis results, were conducted. The research results showed that it is possible to offset approaches laterally on roundabouts with outer radii between 15 and 25 m, depending on the design vehicle, and that the allowable offset values increase proportionally with the roundabout outer radius. The analysis results were used to create a design model for urban single-lane roundabouts with lateral approach leg offsets enabling their adaptation to spatial constraints while maintaining safe operating speeds.

Infrastructures doi: 10.3390/infrastructures11050178

Authors: Li-Tao Yu Chunbin Yu Fawas. O. Matanmi Zhiping Lin

The application of ultra-high performance concrete (UHPC) in the hogging moment region significantly enhances the crack resistance of concrete slabs of composite girders with integrated piers, while also providing economic benefits. To investigate the crack resistance performance and develop a calculation method for crack width in hogging moment region of steel–UHPC–normal concrete (NC) composite girders, a full-scale bending test was conducted. Based on the test results, the post-cracking residual tensile strength of UHPC was determined according to the energy equivalence principle. A calculation method for reinforcement stress incorporating the tensile contribution of UHPC at a cracked section was proposed and then the applicability for current design codes for crack width calculation was evaluated. For the UHPC–NC interface, a corresponding crack width calculation method was developed. The results indicate that cracks initiated on the surface of the NC layer beneath the UHPC overlay at the cantilever root. Then cracks developed in sequence at the top surface of the UHPC layer cantilever root, the UHPC–NC interface, and the mid-plane of the girder-to-pier joint. Ultimately, UHPC cracks exhibited a “numerous and closely spaced” distribution, whereas NC cracks were “few and widely spaced.” When the residual tensile strength of UHPC at cracked section was considered, the mean value and average coefficient of variation in the ratios of calculated to measured reinforcement stresses for different sections were 1.07 and 0.10, respectively, which can be further used for crack width calculation. The mean ratios of code-predicted to measured UHPC crack widths for different sections using the Chinese code, French code, and European code were 1.10, 0.98, and 1.13, respectively, with corresponding average coefficients of variation of 0.25, 0.33, and 0.28; the Chinese code is recommended for UHPC crack width prediction. For the UHPC–NC interface, an expression for crack width calculation was derived using the comprehensive theory, and the mean ratio of calculated to measured values and the coefficient of variation were 1.08 and 0.18, respectively, demonstrating good predictive accuracy.

Infrastructures doi: 10.3390/infrastructures11050177

Authors: Erfan Ramezanpour Alexander Hainen

Operational shutdowns following hazardous liquid pipeline incidents are critical but poorly understood events that impact the U.S. energy supply. Although prior research has investigated the causes and outcomes of pipeline failures, limited work has explained what drives both the likelihood of a shutdown and the duration once it begins. The goal of this study is to address this gap by developing a hurdle regression model to examine the two-stage shutdown mechanism in pipeline incidents, using the Pipeline and Hazardous Materials Safety Administration (PHMSA) incident dataset from 2010 to 2025. The hurdle model consists of a logistic regression restricted to pre-decision predictors to model the probability of shutdown, and a lognormal regression to model the duration of those leading to shutdown. The results revealed that distinct factors are associated with each outcome. Shutdown probability is associated with pre-decision operational and contextual indicators, including operating pressure at the time of incident, accident type, location, monitoring presence, and response delay. In contrast, shutdown duration is associated with logistical complexity and post-incident severity, including incidents at pipeline crossings, pressures exceeding 110% of the maximum operating pressure, and reported property damage. These findings, while exploratory in nature given the use of public incident data, offer practical reference points for operators and regulators who aim to shorten recovery time and strengthen the resilience of energy infrastructure.

Infrastructures doi: 10.3390/infrastructures11050176

Authors: Thanapong Champahom Chamroeun Se Supanida Nanthawong Panuwat Wisutwattanasak Chinnakrit Banyong Sajjakaj Jomnonkwao Vatanavongs Ratanavaraha

Crashes involving freight and commercial vehicles impose substantial human and economic costs, yet most severity studies pool vehicle types or focus exclusively on heavy trucks, masking class-specific risk mechanisms. This study estimates separate Random Parameters Binary Logit models with heterogeneity in means and variances for three vehicle categories—heavy-duty multi-axle trucks (n = 6512), two-axle trucks (n = 2656), and light-duty pickup trucks (n = 23,477)—using 32,645 crash records from Thailand’s national highway network (May 2022–December 2024). Pairwise transferability tests rejected parameter transferability, with four of six comparisons exceeding the 97 percent confidence level (three of these above 99 percent; χ2 = 85.38 to 240.01), confirming that disaggregate estimation is statistically warranted. Three core findings emerge: First, although barrier medians, cut-in-front maneuvers, and sideswipe crashes affect severity in consistent directions across all vehicle types, their magnitudes differ sharply: the protective effect of barrier medians is nearly six times larger for two-axle trucks (ME = −0.160) compared to heavy-duty trucks (ME = −0.028). Second, several determinants are class-specific: dark unlit conditions elevate severity only for two-axle trucks (ME = 0.128), flush medians only for heavy-duty trucks (ME = 0.040), and raised medians only for light-duty pickups (ME = 0.042). Third, no random parameter is common to all three models. Pooled models, therefore, impose misleading homogeneity assumptions; vehicle-type-specific estimation is essential for targeted safety policy.

Infrastructures doi: 10.3390/infrastructures11050175

Authors: Ziye Lan Shuyi Wang Yinzhu Zhao Yimeng Liu Yuanwen Lai

Synchronous transfer has been widely studied in public transport scheduling, with most research focusing on coordination among conventional bus lines. However, with the rapid expansion of urban rail transit systems, metro–bus transfers have become increasingly important for enhancing overall urban public transport network performance. This study investigates the maximum synchronous transfer problem between metro and conventional bus services under passenger flow constraints. Considering the large transfer demand and the pulse-arrival characteristics of metro trains, a passenger waiting constraint at bus stops is incorporated to reflect capacity limitations and crowding effects. A passenger-flow-constrained maximum synchronization model is formulated to optimize bus departure times without increasing service frequency. Dongjiekou Metro Station and three surrounding pairs of bus stops are selected as a case study. Model parameters are determined through field surveys and operational data. The Grey Wolf Optimizer (GWO) and a simulated annealing–improved Grey Wolf Optimizer (SA-IGWO) are employed to solve the proposed model. The results show that both algorithms significantly improve synchronized transfer volumes by adjusting departure times without increasing service frequency. Compared with the original schedule, the SA-GWO achieves an improvement in synchronization performance ranging from 45% to 50%, outperforming the standard GWO.

Infrastructures doi: 10.3390/infrastructures11050174

Authors: Gadel Baimukhametov Greg White

Runway microtexture is a key parameter governing pavement friction. In recent years, several microtexture assessment methods have been developed; however, understanding of microtexture evolution under operational conditions, as well as the effects of maintenance techniques, remains limited. In this study, a runway at an Australian airport was investigated using laser profilometry. Measurements were conducted across multiple transverse sections, including aircraft touchdown and mid-runway zones. Microtexture deterioration rates were evaluated based on the estimated number of tire–pavement contacts, and aggregate polishing was assessed at different locations. Measurements were also performed after rubber contamination removal and rejuvenation treatments. The results indicate that approximately 25% of total microtexture reduction can be attributed to surface polishing, with a lower contribution in touchdown zones due to the protective effect of rubber deposits. A non-linear degradation trend was observed in touchdown zones, where approximately 1100 tire contacts reduced average microtexture roughness from 18 μm to 11 μm. Rubber removal effectively restored microtexture close to its original levels across the runway width. A rejuvenation treatment with a covering of fine sand initially improved microtexture; however, rapid deterioration occurred due to loss of the sand coating. These findings improve the understanding of microtexture evolution under operational runway conditions, albeit only at a case study level, and support more effective runway maintenance planning and intervention strategies.

Infrastructures doi: 10.3390/infrastructures11050173

Authors: Osama A. I. Hussain Robert C. Moehler Stuart D. C. Walsh Dominic D. Ahiaga-Dagbui

This study evaluates the adoption and efficacy of the 5th Dimension Building Information Modelling (5D-BIM) as a cost dimension for mega rail projects, extending the discussion beyond just technological implementation to consider broader policy and practical implications. The purpose of this article is to understand the governance context of 5D-BIM implementation for rail and transport projects and evaluate the effectiveness of the 5D-BIM framework as currently applied by conducting semi-structured interviews with key stakeholders. Drawing on semi-structured interviews with 22 stakeholders across government, industry, and technology providers, the research examines current 5D-BIM practices. While the primary focus of the research is 5D BIM implementations within the state of Victoria, Australia, which is currently experiencing a surge in rail projects, interviews were also conducted with additional stakeholders from international rail projects for context. The findings reveal fragmented adoption, varying levels of organisational maturity, and significant policy and implementation gaps, particularly in the role of government as the primary client of transport infrastructure. The results of the interviews emphasise the centrality of government and regulatory context in driving the adoption and implementation of 5D-BIM as the primary client of transportation infrastructure and identify actionable recommendations for policymakers and practitioners towards a more integrated approach to 5D-BIM in mega rail projects. While 5D-BIM demonstrates clear benefits in enhancing cost estimation, coordination, and decision-making, its effectiveness is constrained by the absence of clear standards, limited BIM literacy, and inconsistent regulatory guidance. This study provides one of the first empirical validations of the 5D-BIM governance framework, demonstrating that its success is driven less by technological capability and more by policy alignment, standardisation, and institutional leadership.

Infrastructures doi: 10.3390/infrastructures11050172

Authors: Assel Sarsembayeva Saltanat Mussakhanova Darkhan Sakanov Iliyas Zhumadilov Gulizat Orazbekova

Frost heave in cold-region pavements is governed by coupled heat and moisture migration, but the specific contribution of vapour transport in multilayer subgrades remains poorly constrained. This study combines field temperature monitoring with analytical modelling to estimate effective thermal conductivities of pavement structural layers and to evaluate vapour-driven moisture fluxes during seasonal freezing. A vertical thermistor array beneath a two-lane highway near Astana (Kazakhstan) and in the adjacent snow-covered ground is used to back-calculate layer-specific conductivities from midwinter temperature gradients by applying Fourier’s law under quasi-steady conditions. Vapour migration is then assessed by two complementary approaches. A diffusion-based formulation, which couples measured vapour-density gradients with air-filled porosity, provides a conservative lower bound and yields very small fluxes, with maximum daily ice deposition of 8.17 × 10−5 kg·m−2·day−1 beneath the pavement and cumulative seasonal masses of order 10−2 kg·m−2 (10−3 kg·m−2 under snow). An energy-balance approach, which relates conductive heat flux to latent heat of vapour–ice phase change and introduces an efficiency parameter α, supplies a physically constrained upper envelope. For a central scenario with α = 0.6, daily deposition in the 0.60–1.00 m layer reaches 0.0961 and 0.0330 kg·m−2·day−1 beneath pavement and snow, respectively, yielding seasonal totals of 12.1 and 4.1 kg·m−2. Together, these bounds indicate that vapour migration beneath pavements, although unlikely to be the dominant driver of frost heave, can be substantially more intense than under adjacent snow-covered ground due to steeper temperature gradients in the upper subgrade.

Infrastructures doi: 10.3390/infrastructures11050171

Authors: Hatem Affes Salem Georges Nehme

Current industry standards for evaluating concrete durability in wastewater environments, such as ASTM C267, rely almost exclusively on mass loss as the primary performance indicator. This study demonstrates that mass change alone can be an ambiguous metric that does not fully characterize the structural degradation of advanced cementitious binders. Through a comprehensive physical, chemical, and mechanical evaluation of 27 binary and ternary mixtures (totalling 486 specimens), we identify four limitations of mass-based standards: (1) The Slag Anomaly, where excellent surface mass preservation masks a significant loss of internal structural capacity, indicating potential internal structural softening. (2) The Sewage Anomaly, where specimens in active biogenic environments exhibit mass gain (up to +1.21%) despite continuous chemical attack. (3) Non-Linear Scaling, where 5% “accelerated” acid tests fundamentally alter degradation kinetics compared to realistic 1% environments. (4) The Maturation Conflict, where extended curing (56 days) significantly improves the physical resistance of slow-reacting pozzolans (cyment) while increasing the mass loss of high-performance ternary blends (MK/SF), likely linked to the exhaustion of their chemical buffering capacity. Current standards relying solely on mass loss may not capture internal degradation in slag-based cements that remain geometrically intact. We propose residual flexural strength as a necessary complementary metric.

Infrastructures doi: 10.3390/infrastructures11050170

Authors: Mattia Mairone Gaetano Maragno Davide Masera Mauro Corrado

Hybrid reinforced concrete (HRC) sections combining steel and fiber-reinforced polymer (FRP) bars provide a structural solution that balances durability, load-bearing capacity and energy dissipation. However, the absence of unified design provisions and the coexistence of distinct safety formats in European and American codes complicate the consistent assessment of ultimate limit state behavior under combined axial force and bending moment. In this study, a strain-based sectional model founded on compatibility and internal force equilibrium is implemented through a layer-by-layer numerical integration procedure to generate axial force–bending moment (N–M) interaction domains and moment–curvature (M–χ) relationships. The formulation is extended to a dimensionless framework in terms of normalized axial load, bending moment, total hybrid mechanical reinforcement ratio ωh and hybridization parameter R. The analysis is conducted within two regulatory formats: the European framework based on Eurocode 2 and CNR-DT 203 R1/2026 and the American framework based on ACI 318-25 and ACI 440.11-22. The results show that increasing ωh leads to a progressive expansion of the interaction domain and modifies the transition between FRP rupture-controlled and steel-yielding-controlled limit states. Increasing R shifts balanced conditions towards higher axial compression and bending levels. Differences between the two regulatory approaches are observed in terms of predicted curvature capacity and design resistance within the N–M domain, reflecting the distinct safety formats adopted. The proposed dimensionless parametric formulation enables consistent comparison of hybrid configurations and provides basis for interpreting failure-mode transitions and deformation capacity of HRC sections under combined axial and flexural actions.

Infrastructures doi: 10.3390/infrastructures11050169

Authors: Jing Guan Zhenchao Yang Congcong Wang Yisheng Liu

In smart construction, identifying the competencies required of engineering–procurement–construction (EPC) safety directors is important for improving personnel selection, training, and safety-governance effectiveness. Drawing on dynamic capabilities theory, this study develops an exploratory competency framework for EPC safety directors in smart-construction contexts. A mixed-method design was adopted, combining a structured literature review, bibliometric mapping with CiteSpace, semistructured interviews, expert review, and questionnaire-based item screening. Questionnaire data from 189 valid respondents were analyzed using descriptive statistics, item analysis, Cronbach’s alpha, and KMO/Bartlett tests to preliminarily assess the internal consistency and structural suitability of the proposed indicators. The results indicate that the retained exploratory framework comprises three higher-order dimensions—sensing, seizing, and reconfiguring—covering six competency elements and eighteen indicators after the remaining trend-sensing indicator was integrated into data analytics. Compared with conventional safety-management competency frameworks, the proposed framework places greater emphasis on data analytics, intelligent systems application, and cross-departmental coordination in digitally enabled project environments. The framework can be implemented as a role-profile template for recruitment, training-needs diagnosis, and performance appraisal of EPC safety directors, while further empirical validation is required before it is used as a standardized measurement scale.

Infrastructures doi: 10.3390/infrastructures11050168

Authors: Promothes Saha

Pavement management systems (PMS) are essential for formulating a cost-effective capital improvement plan (CIP) that adheres to budget constraints. Optimization techniques are vital in enhancing the efficiency of these plans. Among the various methods available, genetic algorithms (GA) are particularly effective at identifying optimal solutions in complex scenarios. This study introduces a GA-based priority optimization model designed to select the most beneficial road improvement projects while staying within budgetary limits. The model was applied to the extensive road network of Fort Wayne, Indiana, considering critical factors such as budget allocation, roadway classification, PASERs, treatment options, and associated costs. The results demonstrate the model’s effectiveness in prioritizing projects, ensuring that available funds are utilized to achieve maximum impact on roadway conditions. By leveraging GA, this approach not only enhances decision-making processes but also provides a robust framework for future pavement management efforts. Overall, the integration of genetic algorithms into PMS can lead to more strategic and economically sound infrastructure improvements.

Infrastructures doi: 10.3390/infrastructures11050167

Authors: Seyed Sasan Khedmatgozar Dolati Adolfo Matamoros Wassim Ghannoum

The journal retracts the article “Guidelines for Nonlinear Finite Element Analysis of Reinforced Concrete Columns with Various Types of Degradation Subjected to Seismic Loading” [...]

Infrastructures doi: 10.3390/infrastructures11050166

Authors: Taciano Oliveira da Silva Klaus Henrique de Paula Rodrigues Heraldo Nunes Pitanga Francisco Aureliano Rocha de Vasconcelos Teixeira Kelbia da Silva Santos Paulo Roberto Borges Gustavo Henrique Nalon Karine de Oliveira Santos

This study presents a comparative evaluation of the structural performance of flexible pavements made from different hot mix asphalt (HMA). HMAs were proportioned using the conventional Marshall method and HMAs subjected to short-term aging were analyzed. Grades B (binder course) and C (surface course), according to DNIT specifications, were used. After determining the aggregate gradation and asphalt content using the Marshall method, test specimens were produced and tested in the laboratory to determine the mechanical parameters characteristic of each HMA (stability, tensile strength by diametral compression, resilient modulus, fatigue behavior, and permanent strain). The Elsym5 software was used to carry out a structural analysis of an assumed pavement, whereby only the mechanical properties of the surface course and the binder course were varied. The results showed that short-term aging significantly affected the mechanical behavior of HMA and the structural response of flexible pavements. Better structural performance was observed in HMAs subjected to short-term aging. The aged specimens showed an improvement in mechanical properties compared to specimens produced by the conventional method, indicating a promising approach for optimizing pavement performance. These results provided new parameters for investigation and development in the field of road engineering.

Infrastructures doi: 10.3390/infrastructures11050165

Authors: Nour Bassim Frahat Mohamed Samy Mohamed Amin Ibrahim Saad Agwa Engy M. Kassem

This study extends beyond traditional single-binder assessments by developing a mechanistic framework for interpreting the behavior of multi-component geopolymer systems. It systematically examines the roles of industrial by-products (granulated blast-furnace slag), agricultural residues (barley straw ash), and construction-derived materials (recycled granite powder) when integrated into a metakaolin-based matrix, with particular emphasis on their influence on gel formation pathways, microstructural refinement, and macroscopic performance. A sustainable geopolymer concrete (SGC) system was formulated using multi-binder combinations at replacement levels ranging from 5% to 30%. Comprehensive evaluations were conducted, including fresh properties, mechanical performance, durability characteristics, thermal resistance, and microstructural features. The results demonstrate that the 70Mk–30GBFS composition facilitates the development of a dense hybrid C–(A)–S–H/N–A–S–H gel network, resulting in a 26.8% enhancement in compressive strength and a 32.0% decrease in chloride ion penetration. Rather than depending on empirical relationships, the study establishes a mechanistically grounded link between precursor chemistry, interfacial transition zone (ITZ) refinement, and performance limits. These findings contribute to a deeper understanding of multi-component geopolymer design and support the development of high-performance, sustainable concrete materials for structural applications.

Infrastructures doi: 10.3390/infrastructures11050164

Authors: Antonio Bilotta Andrea Pollastro Ivan Di Cristinzi Maria Rosaria Pecce

Vibration-based damage detection methods are increasingly recognized as effective tools for monitoring the structural health of bridges. However, their reliability and applicability to various types of structural defects require further study, especially based on experimental tests, to correctly interpretate the results and compare the efficiency of different damage indexes. In the field of Structural Health Monitoring (SHM) by dynamic techniques, operational modal analysis (OMA) is of particular interest because only ambient signals are used, avoiding the service interruption of the infrastructures. However, the key issues of an efficient SHM are the possibility to have a quick alarm if an anomalous response is detected and the capability to localize the defect. Several methods can be applied for the anomaly detection considering machine learning, moving further than global modal parameters like the vibration frequency. Conversely for defect localization, local modal parameters, like modal curvature, can be efficient but also a different application of machine learning can be considered. In this paper, two approaches are compared for level 1 (detection) and 2 (localization) damage detection using acceleration measurements: the modal parameters and an Artificial Intelligence (AI)-based procedure using Variational Autoencoders (VAEs). The case study is a set of post-tensioned prestress concrete (PC) beams that represent a wide stock of existing bridges characterized by defects due to a reduction in the prestressing load, a lack of mortar in ducts, and corrosion of tendons. The results show that both methods can be effective, even if defects in PC beams are difficult to be detect with the dynamic response. Finally, the AI-based approach seems a promising solution because I allows for an earlier alarm, even with few sensors, while the modal curvature approach provides a better explanation of the identified anomaly, although it requires a greater number of sensors.

Infrastructures doi: 10.3390/infrastructures11050163

Authors: Jawad H. Gull Sana Amir Qasim Shaukat Khan

Integral abutment bridges (IABs) eliminate deck joints by rigidly connecting the superstructure to the abutments, reducing maintenance costs but introducing thermal restraint forces. When only one abutment is made integral, all thermally induced longitudinal movement concentrates at the remaining non-integral end, overloading bearings and concrete elements not designed for this condition. This paper investigates IAB behavior and evaluates two repair options for two, three-span continuous steel bridges on Interstate 635 in Kansas City, Kansas, which sustained progressive abutment damage following a unilateral integral conversion in 2005. A 2D finite element model was developed in LARSA 4D, incorporating composite superstructure elements, shell element abutments, beam element piles, and soil-structure interaction via distributed lateral springs. The model was analyzed under dead, live, braking, and thermal load combinations in accordance with AASHTO LRFD. Full integral conversion generates thermal restraint moments of approximately 813.5 kN-m (600 kip-ft) at the abutments, and pile stresses of 383.9 MPa (55.68 ksi) under Service I and 497.4 MPa (72.14 ksi) under Strength I combinations, both exceeding allowable limits. Elastomeric bearing pads at the non-integral abutment satisfied all stress limits without foundation modification and are recommended as a practical repair strategy for bridges in similar conditions.

Infrastructures doi: 10.3390/infrastructures11050162

Authors: Shuo Liu Jingbo Qing Jiabin Liu

Meteorological archives often preserve abundant air-temperature records. However, verified pavement distress records are often unavailable. This makes it difficult to translate archive-scale temperature data into material thermal states that can support engineering screening and interpretation. This study develops a temperature-only probabilistic framework that links a national daily air-temperature background with asphalt and concrete thermal states through site-specific calibration. Northeast China was selected as the case study region, where synchronous 5-min observations of air, concrete, and asphalt temperatures were available from 2024 to 2025. Nationwide daily records from 1951 to 2019 place the air-temperature exposure background of the case study region in a national context. The case study region does not emerge as a dominant national hot-tail regime. Instead, it is characterized by colder minima and larger daily air-temperature ranges than the pooled national background. Under the same air-temperature exposure, asphalt showed stronger amplification of thermal peaks and diurnal cycling than concrete. In the case study region, both materials show a consistently cold-dominant screening pattern, with fluctuation screening secondary and hot screening limited. This qualitative ordering is preserved across weighting, archive-window, and transfer model sensitivity analyses, although hot and fluctuation magnitudes are less stable than the cold side estimates. The framework should therefore be interpreted as a thermal screening tool calibrated at a single monitored site, rather than as a universally validated distress or failure model.

Infrastructures doi: 10.3390/infrastructures11050161

Authors: Guangyu Li Hai-Bin Huang Shengzhi Ai Yuan Cheng Dong Liang

The rigid-body displacement of bridge girders, particularly the lateral displacement of curved girder bridges, is a critical indicator reflecting the structural safety reserve and durability of bridges. However, under long-distance imaging conditions, the inherent scale ambiguity and perspective distortion in monocular vision measurement, coupled with environmental interferences such as weakened natural edges and varying illumination, pose severe challenges to target-free, high-precision, and real-time displacement measurement. To this end, this paper proposes a target-free visual method for measuring rigid-body displacement of bridge girders under long-distance imaging. By fusing optical flow and Hough transform to extract seismic block edges and adopting hierarchical NCC matching for stable girder tracking, the method achieves millimeter-level accuracy, real-time performance, and strong illumination robustness. Model tests and field validation confirm its effectiveness for low-cost bridge health monitoring.

Infrastructures doi: 10.3390/infrastructures11050160

Authors: Xueyu Ren Jiawang Chen Shang Sun Jianling Zhou Zhonghui Zhou Yuan Lin

Subsea oil and gas pipelines are critical infrastructure in marine engineering, and strain monitoring is essential for their safe operation. However, due to the complexity of the marine environment and the constraints practical deployment, engineering applications often rely on sparse monitoring points, making it difficult to directly obtain full-field strain information. To address this issue, this paper proposes a strain field reconstruction method for subsea suspended pipelines based on the inverse finite element method (iFEM) and a greedy search strategy, and provides the corresponding optimal layout of monitoring cross-sections. Using a constructed numerical simulation library under multiple load cases, algorithm validation and parameter calibration are performed. On this basis, a comprehensive evaluation framework incorporating both global and peak errors is established. Results show that under the greedy-optimized monitoring section scheme, the comprehensive reconstruction error of iFEM ranges from 0.030 to 0.035, the axial strain error is significantly lower than the circumferential strain error, and the peak relative error stabilizes when the number of monitoring sections reaches seven. The proposed method overcomes the difficulty of acquiring full-field strain information under sparse monitoring conditions, and can provide technical support for the structural health monitoring and safety assessment of subsea oil and gas pipelines.

Infrastructures doi: 10.3390/infrastructures11050159

Authors: Muhammad Ali Musarat Ruben Paul Borg Jingjie Wei Carl James Debono Kamal Khayat

3D printing is evolving at a fast pace in both the manufacturing and construction sectors. These advancements can greatly benefit these industries. However, the 3D printing of concrete structures presents some challenges due to defects in the 3D concrete printed elements. Hence, this study systematically reviews Artificial Intelligence (AI)-driven techniques, such as Computer Vision and Machine Learning, to identify surface defects that can occur in 3D-printed cementitious material structures. The adopted methodology was the PRISMA statement with the aim of reporting the systematic review and meta-analysis. Two well-known databases, Web of Science and Scopus, were utilised for data extraction of articles published during the past 10 years, between 2014 and May 2025. The initial search provided 110 articles, both conference and journal papers; after screening, only 11 were left for the final review assessment. The smaller number of the final articles shows that much work is still needed in this area. It has been observed that various computer vision and machine learning-based methodologies were employed to classify defects in 3D concrete printed structures. Deep learning algorithms, such as YOLO and RT-DETR, were featured as the most efficient in real-time defect detection and quality monitoring. It was also observed that real-time monitoring systems attached to 3D printers help in reducing the material wastage, which is essential to meet the sustainable goals. However, more work is still required to underline the defects of 3D-printed cementitious material, probably with the involvement of AI image processing tools and techniques. This can help to automate the defects in 3D-printed structures, and by this, the productivity could be enhanced.

Infrastructures doi: 10.3390/infrastructures11050158

Authors: Hugo Martínez Ángeles Cesar Augusto Navarro Rubio José Gabriel Ríos Moreno Margarita G. Garcia-Barajas Roberto Valentín Carrillo-Serrano José Luis Reyes Araiza Ernesto Chavero-Navarrete Mario Trejo Perea

The construction sector is responsible for significant global energy consumption and CO2 emissions, largely driven by carbon-intensive materials such as ordinary Portland cement and steel. In response to increasing decarbonization and circular economy demands, several strategically relevant categories of sustainable construction materials have been developed, particularly natural and bio-based systems, recycled and waste-derived materials, low-carbon cementitious binders, and emerging multifunctional composites. However, research remains fragmented across material classes and performance metrics. This systematic review evaluates advances published between 2018 and 2026 following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 methodology. Peer-reviewed studies were systematically identified and analyzed to compare mechanical performance, durability, embodied carbon reduction, and life-cycle environmental impacts across these selected material pathways. The results indicate substantial decarbonization potential. Low-carbon cementitious materials report CO2 reductions of approximately 10–75% relative to conventional systems, while engineered timber and bamboo demonstrate 28–70% lower carbon footprints due to reduced embodied energy and biogenic carbon storage. Recycled aggregates and industrial by-products enhance circularity but remain sensitive to transport distance and processing intensity. Trade-offs between mechanical capacity and environmental performance are evident in lightweight and bio-based systems. Overall, sustainability gains are maximized through integrated hybrid construction strategies rather than isolated material substitution. This review provides a comparative evidence-based synthesis and identifies key research gaps and implementation challenges for accelerating low-carbon construction.

Infrastructures doi: 10.3390/infrastructures11050157

Authors: Christina N. Tsaimou Vasiliki K. Tsoukala

Ports are complex infrastructure systems operating under adverse marine environments, diverse loading regimes, and significant economic pressures. Among their critical assets are pavement infrastructures that serve multiple functional domains, including container handling and storage areas, internal circulation corridors, passenger–vehicle interfaces, and auxiliary parking zones. However, existing port pavement research remains predominantly concentrated on heavy-duty container applications, while other functional categories are comparatively underexplored. This study develops a structured engineering synthesis of port pavement infrastructure assets by integrating bibliometric mapping, conducted using Scopus-indexed publications, with a functional–structural analysis of worldwide practices. Following the identification of research trends, additional insights from engineering-oriented studies and technical guidance documents were incorporated to strengthen the practical relevance of the investigation. These findings indicate that functional classification should precede structural design decisions, enabling the systematic identification of loading conditions, serviceability requirements, and transition demands across port environments. Heavy-duty operational zones require high-stiffness systems capable of resisting concentrated and repetitive loads, while circulation areas are particularly sensitive to low-speed traffic effects. In contrast, passenger and mixed-use zones necessitate hybrid design strategies that balance structural adequacy with serviceability and long-term durability under marine exposure, whereas auxiliary areas are primarily governed by cost-efficiency and maintenance considerations. The overall research provides a rational basis for investment prioritization, material selection, lifecycle planning, and performance-based pavement management within multifunctional port environments.

Infrastructures doi: 10.3390/infrastructures11050156

Authors: Eduardo Dávila Brad D. Weldon Paola Bandini Michael J. McGinnis Brittany K. Bullard

This study tested quarter-scale adobe masonry walls under monotonic in-plane loading, considering the effect of water content at the foundation–wall interface, fiber type, and openings (i.e., door, window). Seven walls were constructed with unstabilized adobe bricks containing either cut straw or sisal fibers and mud mortar. Gravimetric water content (wb) at the foundation–wall interface (i.e., wall base) varied by test wall, ranging from 2.4 to 4.9% by dry mass. The walls were instrumented to measure in-plane and out-of-plane displacements and vertical deflections during the load tests. Greater water contents at and near the wall base shifted cracking toward the lower courses and along the foundation–wall interface; however, the peak load capacity did not vary significantly with wb but was strongly influenced by crack trajectory, including whether cracking diverted into the foundation or propagated rapidly along the foundation–wall interface. Peak loads ranged from 1928 N (433 lb) to 6517 N (1465 lb). Fiber type influenced deformation behavior of the walls, with sisal-brick walls generally developing larger vertical deflections and, in some instances, larger peak in-plane displacements than straw-brick walls. Window and door openings altered crack initiation and propagation by concentrating cracking at opening corners and producing segmented mechanisms, increasing in-plane displacements in some cases, but still sustaining comparatively large peak loads.

Infrastructures doi: 10.3390/infrastructures11050155

Authors: Sofia Borgosano Andrea Di Martino Michela Longo

The decarbonization of urban public transport requires robust tools to evaluate the operational feasibility and energy implications of bus electrification. This study presents a physics-based modeling framework for estimating the energy consumption of urban bus operations using real-world telemetry data. GPS measurements collected onboard operating buses are used to reconstruct vehicle speed profiles and driving dynamics. The methodology is applied to a representative urban bus route operating in the city centre of Milan, characterized by dense traffic, closely spaced stops, and a high density of signalized intersections. Two operational improvement scenarios are investigated: traffic signal coordination through a “green wave” strategy and the integration of opportunity flash charging (OC) at selected stops. The results show that reducing traffic-related stops improves commercial speed and decreases energy demand, while OC can support battery operation within the constraints of urban service conditions. The proposed framework provides a transferable decision-support methodology for transit agencies planning the electrification of urban bus services and the deployment of supporting infrastructure.

Infrastructures doi: 10.3390/infrastructures11050154

Authors: Suphawut Malaikrisanachalee Auckpath Sawangsuriya Phansak Sattayhatewa Ponlathep Lertworawanich Apiniti Jotisankasa Susit Chaiprakaikeow Narongrit Wongwai

This study presents a data-driven framework for the mechanistic interpretation of asphalt pavement responses using an integrated smart sensing and monitoring system deployed on a national highway in Thailand. A fully instrumented pavement test section was developed, incorporating a multi-sensor embedded network and a field data acquisition platform integrated with weigh-in-motion (WIM) technology. The system consists of 54 sensors, including strain gauges, pressure cells, moisture sensors, and thermocouples, installed at multiple depths to capture high-resolution stress–strain responses under controlled heavy-vehicle loading. Field measurements were analyzed and compared with classical mechanistic models, including Boussinesq’s theory, Odemark’s equivalent thickness method, and Burmister’s multilayer elastic theory. The results demonstrate good agreement for vertical stress predictions in deeper layers, while significant discrepancies were observed in strain responses, particularly in the asphalt layer, where measured tensile strains were up to 2.5 times higher than theoretical estimates. The findings indicate that conventional elastic models provide useful first-order approximations; however, discrepancies were observed in representing the viscoelastic behavior of asphalt materials under real loading conditions. Furthermore, the integration of sensor data with traffic loading information confirms that axle load magnitude is the dominant factor governing pavement responses, whereas vehicle speed primarily influences load duration. The proposed framework demonstrates the potential of smart sensing systems for enabling automated, data-driven pavement analysis and supporting digital twin-based infrastructure management.

Infrastructures doi: 10.3390/infrastructures11050153

Authors: Genadijs Sahmenko Girts Bumanis Maris Sinka Peteris Slosbergs Alise Sapata Diana Bajare Vjaceslavs Lapkovskis

Ternary gypsum–cement–pozzolan (GCP) binders represent a promising low-carbon alternative to traditional Portland cement-based systems for additive 3D printing (3DP). This study presents a systematic three-stage experimental framework for the development of printable and durable GCP mixtures: (i) optimisation of gypsum–cement–metakaolin binder proportions based on a ternary diagram for 25 formulations, (ii) comparative evaluation of different pozzolanic additives and secondary gypsum sources alongside comprehensive durability testing, and (iii) adaptation of the optimised mixtures for 3DP, focusing on rheological properties. The optimal composition was determined with 55 wt% gypsum, 22.5 wt% Portland cement, and 22.5 wt% metakaolin, achieving a 28-day wet compressive strength of 36.2 MPa and a softening coefficient of 0.85. Successful integration of secondary gypsum sources was demonstrated. The GCP 3DP mixtures were developed with water/binder ratios of 0.38–0.45 and sand/binder ratios of 0.5–1.4, with an open time of 20–40 min. The mixtures exhibit pronounced thixotropic behaviour, characterised by increasing yield stress over time and relatively stable plastic viscosity. Printability tests confirmed the stable application of 29–39 layers before structural buckling. 3DP under laboratory conditions successfully demonstrated the feasibility of producing architectural and structural elements from sustainable GCP compositions.

Infrastructures doi: 10.3390/infrastructures11050152

Authors: Nawal Louzi Areen M. Arabiat Mahmoud AlJamal

This paper presents a novel system-oriented counterfactual deep learning framework, termed Hybrid Prediction–Intervention Neural Architecture (HPINA) for intelligent traffic accident risk prediction and proactive safety intervention in smart transportation systems. Unlike conventional data-driven models that rely solely on observational correlations, the proposed system integrates multi-domain data fusion, temporal deep representation learning, a continuous spatio-temporal risk field, and a latent-space counterfactual reasoning module within a unified decision-support architecture. The framework enables accurate prediction of traffic accident risk and simulation of “what-if” intervention scenarios to support real-time safety optimization in intelligent transportation environments. By leveraging heterogeneous inputs, including traffic dynamics, environmental conditions, road attributes, and temporal patterns, the system constructs a high-dimensional representation that captures complex nonlinear dependencies and evolving risk propagation across the network. A key innovation lies in the integration of a causal intervention mechanism and policy-guided decision layer, which jointly quantify intervention impact and identify optimal strategies for minimizing risk. The experimental results demonstrate that HPINA achieves a Test F1-score of 0.958 and an AUC of 0.989, outperforming strong baselines by up to 5.0% and 3.4%, while achieving a relative risk reduction of 0.091 and improved convergence stability with a validation loss of 0.042. These findings highlight the effectiveness of the proposed framework as an intelligent, scalable, and deployable system for real-world traffic safety management and smart city applications.

Infrastructures doi: 10.3390/infrastructures11050151

Authors: Nasim Shakouri Mahmoudabadi Charles V. Camp Afaq Ahmad

Fiber-reinforced polymer (FRP) composites are widely used to strengthen reinforced concrete (RC) columns due to their high strength, durability, and ease of installation. Accurate prediction of the axial capacity of CFRP-strengthened concrete columns is essential for reliable structural design. Yet conventional empirical models often exhibit limited accuracy due to the complex interactions among structural parameters. This study develops a deep learning-based model to predict the axial capacity of CFRP-wrapped RC columns using a database of 469 experimental tests collected from published studies. A deep neural network (DNN) was optimized using the Optuna hyperparameter tuning framework and k-fold cross-validation to enhance model accuracy and robustness. Model performance was evaluated using statistical indicators, including R2, RMSE, MAE, MAPE, and the a20-index. The results show excellent predictive performance with R2 values approaching 0.99 and an a20-index of 0.98, demonstrating strong agreement between predicted and experimental results. Comparisons with the ACI 440.2R-17 and CSA S806-12 design codes indicate that the proposed DNN model provides significantly improved prediction accuracy, with lower errors. The developed approach offers a reliable and efficient tool for estimating the axial capacity of CFRP-strengthened concrete columns.

Infrastructures doi: 10.3390/infrastructures11050150

Authors: Andrzej Szymon Borkowski

The construction sector has long been perceived as one of the least digitized branches of the economy [...]

Infrastructures doi: 10.3390/infrastructures11050149

Authors: Rabie A. M. Amnisi Mohamed E. El-Zoughiby Basem S. Abdelwahed Osama Youssf

Engineered cementitious composite (ECC) is an advanced material known for its superior flexibility, high durability, and crack resistance, making it ideal for a variety of structural applications. However, it uses cement at a rate of 2–3 times more than conventional concrete which raises environmental concerns. This study focused on the production of eco-friendly ECC by incorporating various waste materials as partial cement and sand substitutes. Cement kiln dust (CKD), ceramic powder waste (CPW), and eggshell waste (ESW) were used as partial substitutes for cement in doses of 10% and 20%. Crumb rubber (CR) was used as a partial substitute for sand in doses of 25, 50, 75, and 100%. Chemical treatments using sodium hydroxide, sodium silicate, and a mix of both of them were carried out for the CR in the production of the proposed ECC. Physical treatment using the same cement substitute materials (CKD, CP and ESP) was also carried out for the CR. The effect of fiber type—such as basalt fibers (BF), polypropylene fibers (PPF), and steel fibers (StF)—on the performance of ECC was also investigated. Slump, compressive strength, uniaxial tensile strength, flexural strength, and sorptivity were the measured properties for the proposed ECC. Microstructure analyses were also conducted on some selected ECC mixtures. Among the tested mixtures, the results showed that replacing 10% of the cement with CKD improved the compressive strength by up to 22.6% and the tensile strength by up to 18.3%. Using 50% untreated CR reduced compressive and tensile strength by 32.8% and 28.1%, respectively, compared to the control ECC. The physical treatment of CR using CKD improved the compressive strength by up to 12.7% and the tensile strength by up to 3.2% compared to untreated CR. The microstructure analyses revealed an improvement in fiber-matrix bonding and a reduction in crack width in the mixtures, especially in the BF and PPF blends.

Infrastructures doi: 10.3390/infrastructures11050148

Authors: Mahdi Sadeqi Bajestani Ali Pirdavani

Speed is a key determinant of crash risk and injury severity, particularly on urban and secondary roads with frequent interactions between vulnerable road users. Traffic calming measures (TCMs) encompass physical, regulatory, perceptual, and technological interventions and aim to reduce operating speeds and improve safety and liveability. This study systematically evaluates the effectiveness of TCMs in reducing speed and improving safety outcomes on urban roads, following PRISMA 2020 guidelines. It encompasses the identification, screening, and synthesis of articles from the Scopus, ScienceDirect, and SpringerLink databases, published between January 2020 and February 2026. Risk of bias in the included studies was assessed qualitatively by the co-authors. The assessment was conducted independently, with discrepancies resolved through discussion. A total of 91 studies were included in the review. Evidence from field studies, driving simulator experiments, and analytical, simulation, and computation-based evaluations is reviewed and structured within a three-cluster taxonomy comprising physical and geometrical measures, regulatory and perceptual interventions, and digital and technological approaches. The synthesis indicates that physically self-enforcing measures yield the most consistent reductions in speed. At the same time, regulatory and digital interventions can deliver meaningful safety benefits when implemented at scale with credible governance. Perceptual and advisory measures show more varying and context-dependent effects. The evidence base is limited by heterogeneity in study designs, short-term evaluations, and inconsistent reporting across studies.

Infrastructures doi: 10.3390/infrastructures11050147

Authors: Usama Heneash Alireza Bahrami Mohamed Ghalla Galal Elsamak Ayah A. Alkhawaldeh Ali Basha

Cast-in-place ground-supported concrete slabs (GSCSs) are used as floors in many facilities such as factories, workshops, garages, and airports (i.e., rigid pavements). These slabs may be subjected to repeated impact loads caused by vehicle loads, the dropping of heavy loads, and aircraft landing loads on runways. This research presents an experimental and numerical study to investigate the behavior of these slabs under impact loads. The experimental program consists of 18 concrete slabs with dimensions of 400 mm × 400 mm × 100 mm. Some variables were studied experimentally, such as the reinforcement ratio of these slabs and the amount of the impact force (represented by the drop height). Unreinforced slabs and slabs reinforced with steel reinforcement or a geogrid mesh made of knitted polyester ribs were tested. ABAQUS software was employed to study the failure mode and crack distribution of these slabs numerically. The accuracy of the proposed numerical model was verified by modeling the tested slabs and comparing the numerical results with the experimental results. From the study results, it is clear that the reinforcement significantly improves the impact performance of GSCSs, transforming their failure behavior from brittle to more ductile and tough. The combined use of impact strength and ductility factors provides an integrated measure of slab performance, offering valuable guidance for the design of protective structures, pavements, and industrial flooring under impact loading.

Infrastructures doi: 10.3390/infrastructures11050146

Authors: Sanjog Chhetri Sapkota Moinul Haq Bipin Thapa Sabin Adhikari Anupam Dhakal Roshan Paudel Aashish Ghimire Tushar Bansal

The prediction of the compressive strength (CS) for sustainable concrete reinforced with graphene nanoplatelets (GNPs) is difficult as a result of nonlinear interactions between chemical composition, dispersion state, and curing conditions. To address this, an interpretable ensemble machine learning framework is developed to provide accurate predictions of CS. The major input parameters used are sand content, graphene diameters, graphene thicknesses, and percentages of GNP to sand (GNP%; w/w), water-to-cement ratio W/C, ultrasonication period UST time (s), curing age CA day(s), while the CS (in MPa) is the target output. The random forest (RF) and XGBoost (XGB) models are incorporated into two novel metaheuristic optimization techniques, the Drawer-based optimization algorithm (DOA) and the Giant Trevally Optimizer (GTO), to enhance hyperparameter tuning and generalization. For all models, DOA XGB hybrids are the most predictive, with testing R2 values up to 0.98; RMSE of around 2.9 MPa; MAE is approximately 2.0 MPa, and well over 97% within ±20% prediction error boundaries. The explainable artificial intelligence methodologies like Shapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), partial dependence plots, and Individual Conditional Expectation plots reveal curing age and graphene thickness as the dominant parameters. High strengths above 70 MPa are always achieved from higher curing age, w/c ratio (from 0.3 to 0.4), and graphene dosage (from 0.5 to 2.5%). A Python GUI is developed for efficient and accurate strength predictions suitable for practical applications. The proposed approach provides a robust, interpretable, and efficient alternative to extensive testing for GNP-reinforced concrete.

Infrastructures doi: 10.3390/infrastructures11050145

Authors: Andrzej Szymon Borkowski

Existing BIM (Building Information Modeling) validation mechanisms, namely geometric clash detection and attribute completeness checking of individual objects (MVD, IDS), do not cover a significant category of informational incompleteness: situations in which the properties of interdependent entities become fully defined only as a result of their mutual presence in the model. This article introduces the new concept of ontosaturation as a new mechanism of formal ontology that formalizes this phenomenon. Ontosaturation describes the relationship between existentially independent entities whose certain properties remain undetermined (unsaturated) in isolation and acquire values only after the attributes of related objects are taken into account. The article proposes a formal definition of ontosaturation and the supporting concepts needed to apply it in practice. These include the saturant (an entity that completes the properties of another), the saturation cluster (a group of mutually saturating entities), and the saturation index, a metric enabling a quantitative assessment of the relational completeness of a BIM model at the level of a single entity (s(e)) and the entire model (S(M)). The concept of a saturation profile was also introduced, complementary to the Level of Information Need (LOIN) in accordance with the ISO 19650 series of standards, defining minimum saturation thresholds for successive phases of the project lifecycle. The mechanism was demonstrated using the example of an installation penetration through a fire separation wall, modeled in Autodesk Revit 2025, showing that collision detection and attribute validation fail to detect four unsaturated properties critical to fire safety and structural integrity, which ontosaturation identifies. The proposed approach constitutes a third layer of BIM model validation, alongside the geometric and attribute layers, addressing the relational completeness of information between interdependent objects.

Infrastructures doi: 10.3390/infrastructures11050144

Authors: Andrea Meoni Michele Mattiacci Alina Elena Eva Francesco Falini Filippo Ubertini

Masonry arch bridges are critical assets in aging transportation networks, yet their Structural Health Monitoring (SHM) remains challenging. Smart bricks—piezoresistive sensing units compatible with masonry structures and capable of acting simultaneously as load-bearing components and strain sensors—offer a promising solution for embedding self-sensing capability directly within the masonry. While previous work by the authors has investigated their use in masonry walls, their application to arched structures remains unexplored. This gap is particularly significant given that arches, characterized by a predominantly compressive stress state, represent a natural context for smart-brick implementation. This study presents a numerical investigation assessing the potential of smart bricks for strain-based SHM of masonry arch bridges. A Finite Element (FE) model, derived from a validated experimental benchmark representative of typical Italian railway arch bridges, was used to virtually embed smart bricks at selected cross-sections along the arch. Damage progression was simulated through cyclic loading–unloading stages, enabling direct correlation between strain evolution and structural deterioration. Results demonstrate that smart bricks accurately capture damage-driven strain redistributions, closely mirroring both the sequence of damage formation and the associated collapse mechanism. These findings support the use of smart bricks for early detection of localized structural changes in masonry arches, providing a foundation for future experimental validation and real-world deployment of minimally invasive SHM systems.

Infrastructures doi: 10.3390/infrastructures11040143

Authors: Tianming Miao Junwu Dai Tao Jiang Yongjian Ding Ruchen Qie Yingqi Liu Ying Zhou

The calculation for the local bearing capacity of stirrup-confined concrete is an important issue in structural design. Due to the coupling effects of multiple factors, there is no unified calculation method recognized by scholars. The improved backpropagation neural network model based on the particle swarm optimization algorithm (PSO-BPNN) is used in this research to conduct a systematic analysis. The results of 40 stirrup-confined concrete specimens from the tests conducted by ourselves and an additional 92 similar test data points from references were combined; the calculation efficiency and accuracy of the PSO-BPNN model were verified. Compared with the BPNN model, the training iterations of the PSO-BPNN model were reduced by 74.23% with the condition of same training effect. The mean squared error (MSE) is reduced by 33.9%, and the coefficient of determination (R2) is increased by 5.5% with the condition of the same number training iterations. In addition, compared with the calculation stability and accuracy of Random Forest Regression (RFR), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) models, the PSO-BPNN model also shows better results. Within the applicable range of the codes, the average ratio of the predicted values to the calculated values for GB50010-2010, MC2020 and ACI318-25 are 1.988, 1.719, and 5.387, respectively. A higher evaluation for the contribution of stirrup is considered in the MC2020 code; the predicted values of some specimens are lower than the calculated values when Acor/Al is less than 1.35. The brittleness effect is not adequately considered: the predicted values of some specimens are also lower than the calculated values with the active powder concrete (RPC) is used. The sensitivity ranking of the model with coupling effect for parameters is Al, Ab, fc,k, s, d, dcor, and fy,k. It is slightly different from the sensitivity ranking obtained by analyzing individual parameters, but the calculation logic is consistent. The research results can provide a theoretical basis for practical engineering.

Infrastructures doi: 10.3390/infrastructures11040142

Authors: Guangyu Men Fangyuan Han Yanlin Chen Yu Cui Jialong Yan Juanqi Liang Zichao Wu

To address the growing demand for sustainable road infrastructure development and resolve technical bottlenecks in reclaimed asphalt pavement (RAP) recycling, this study optimized the performance of recycled asphalt mixtures (RAMs) and validated their engineering applicability for field construction. RAM specimens were prepared using 5-year and 10-year aged RAP from Ningxia, with a constant RAP content of 30%. Laboratory tests including high-temperature rutting, moisture susceptibility, low-temperature cracking, dynamic modulus, and four-point bending fatigue were performed to determine the optimal mix proportion. Fourier Transform Infrared Spectroscopy (FTIR) and Thin-Layer Chromatography-Flame Ionization Detection (TLC-FID) were employed to reveal the regeneration mechanism of waste engine oil (WEO). Results showed that WEO modified the functional groups and four fractions of asphalt, optimizing its colloidal structure, while excessive WEO compromised high-temperature stability. The optimal WEO contents were 4% for RAP (5Y) and 8% for RAP (10Y), which significantly enhanced the overall performance of RAM to adapt to Ningxia’s climate. This study provides technical support for sustainable road infrastructure in arid and semi-arid regions.

Infrastructures doi: 10.3390/infrastructures11040141

Authors: Beatriz Ribeiro Josias Breda Francisco Machado Jorge Pais

This paper develops a shock-absorbing asphalt mixture for pedestrian pavements that mitigates the impact of normal walking on pedestrians’ bodies by incorporating crumb rubber from recycled tires to produce a soft mixture. This aims to reduce injuries to vulnerable road users, enable the rethinking of urban pavement designs, and address the major challenges facing societies, ultimately achieving more sustainable, resilient, and safer cities. To promote land sustainability, the designed asphalt mixture should be pervious, allowing water to infiltrate into the underlying soil. The development of the asphalt mixture followed an experimental methodology that involved formulating asphalt mixtures with conventional bitumen, polymer-modified bitumen, and bituminous emulsion. The shock-absorbing capability was evaluated by measuring the deformation of the asphalt mixture over time in response to a falling weight from a Light Falling Weight Deflectometer. Permeability capabilities were assessed through the permeability test. Subsequently, the asphalt mixture was characterized according to its macrotexture, friction, air void content, rutting resistance, and stiffness to assess its suitability as a walking surface material. Results indicate that increasing rubber content enhances deformation capacity and improves cushioning but reduces stiffness. Among the solutions, mixtures with polymer-modified bitumen and intermediate rubber content achieved the balance between impact attenuation and mechanical performance.

Infrastructures doi: 10.3390/infrastructures11040140

Authors: Petr Veselý Ondřej Dašek Martin Jasso

This study investigates the synergistic effects of combining polyphosphoric acid (PPA) and styrene–butadiene–styrene (SBS) as modifiers in asphalt binders to enhance their performance. The research focuses on optimizing the concentrations of PPA and SBS to improve the resistance to permanent deformation, cracking at intermediate and low temperatures, and resistance to aging. A series of empirical and rheological tests, including penetration, softening point, elastic recovery, dynamic shear rheometer (DSR), multiple stress creep recovery (MSCR), and bending beam rheometer (BBR), were conducted to evaluate the rheological and engineering properties of the modified binders. The results indicate that PPA can partially replace SBS, offering comparable improvements in high-temperature performance and creep resistance. The MSCR test revealed a statistically significant synergistic effect between PPA and SBS, resulting in improved recovery and reduced non-recoverable compliance. However, PPA alone shows limited effectiveness at low temperatures and in properties that are governed by elastic response. This study highlights the potential for optimizing asphalt modifiers by leveraging the complementary properties of PPA and SBS in hybrid systems, particularly regarding high-temperature properties and dynamic loading.

Infrastructures doi: 10.3390/infrastructures11040139

Authors: Caroline F. N. Moura Hugo M. R. D. Silva Joel R. M. Oliveira

Asphalt pavements are essential to modern transport infrastructure but remain highly dependent on virgin aggregates and petroleum-based binders, resulting in high energy demand and significant greenhouse gas emissions. In response, research has advanced recycled-material solutions and low-temperature asphalt technologies. However, sustainability is still often inferred from isolated environmental indicators, without consistent consideration of mechanical durability or economic feasibility throughout the life cycle. This review provides an integrated synthesis of sustainable asphalt mixtures by jointly examining recycling strategies, temperature-reduction processes (warm-mix, half-warm-mix, and cold-mix asphalt technologies), and their combined applications through an integrated performance–cost–environment perspective. The literature reveals substantial methodological fragmentation, with limited harmonisation of functional units, system boundaries, and allocation rules, which constrains cross-study comparability. Evidence indicates that reclaimed asphalt, recycled concrete aggregates, and steel slag can maintain or improve rutting resistance, stiffness, and moisture durability while enabling material cost savings of approximately 5–68%. Temperature-reduction technologies further achieve significant energy and GHG reductions in the production phase (20–70%), with integrated recycling–temperature-reduction systems showing the most consistent combined benefits. Overall, this review demonstrates that asphalt sustainability cannot be established through single-dimensional assessments but requires harmonised life-cycle frameworks that explicitly link environmental gains to mechanical performance, durability, and economic viability.

Infrastructures doi: 10.3390/infrastructures11040138

Authors: Patricia Kara De Maeijer Kruthi Kiran Ramagiri Flavio Stochino

In 2026, sustainable construction materials research is focused on optimization of the resources’ circularity, carbon reduction, and performance improvements through advanced materials. Biochar, nanomaterials, and recycled aggregates (RA) are enhancing concrete by improving strength, durability, and carbon capture, while supporting low-carbon, circular practices. When used in low-carbon alkali-activated materials (AAMs), these materials reduce greenhouse gas emissions by approximately 30–60% compared to Portland cement (PC). Despite challenges in cost, standardization, and large-scale production, these innovations are advancing the construction industry towards sustainable, carbon-neutral solutions. RA helps reduce landfill waste and converse resources, though issues like quality variability and potential contaminants must be addressed. Biochar’s (0.5–2 wt.% of binder) adoption is limited by inconsistent properties, while nanomaterials (0.01 to 3 wt.% of binder) offer improved mechanical properties (5–20%) but face high production costs and limited long-term data. In the coming years, efforts will focus on standardizing production, improving nanoparticle dispersion, and refining RA processing. The integration of AI and machine learning may further optimize material design, leading to greener, low-carbon materials for large-scale, sustainable infrastructure by 2036.

Infrastructures doi: 10.3390/infrastructures11040137

Authors: Pengfei Song Zhiwen Tan Bingquan Liu Chengzhi Yi Jia Shi Daibiao Yin Yunchong Peng Junning Xie Junfeng Liu

This paper investigates the influence of three key parameters, which are the spacing of cutters, the dip angle of joints and the spacing of joints on the load evolution process of jointed rock masses from the perspective of rock-breaking mechanics. Furthermore, how variations in cutter spacing and joint characteristics affect cutting efficiency is studied from a macroscopic viewpoint, focusing on indicators such as specific energy (SE) for crack propagation and rock fragment formation. Based on the research results, a novel optimization approach for cutter spacing in jointed rock mass conditions is proposed. The optimal cutter spacings under varying joint conditions are calculated, and the effects of joint spacing and dip angle on cutter spacing optimization are systematically discussed. The results show that when the joint dip angle is 60°, the cutter spacing is 100 mm, and the joint spacing is 30 mm, the rock fragmentation efficiency reaches the highest. It is also found that the influence of the joint dip angle on the optimal cutter spacing is greater than that of the joint spacing. When the joint spacing is 70 mm, the corresponding optimal cutter spacing is 100.7 mm. When the joint dip angle increases from 0° to 60°, the optimal cutter spacing gradually increases to 112.8 mm. When the joint spacing is greater than 60 mm, the optimal hammer spacing of the hammer gradually decreases.

Infrastructures doi: 10.3390/infrastructures11040136

Authors: Tianjiao Zhao Chelsea Buckhalter George Wang

The use of recycled concrete aggregate (RCA) derived from demolished bridges offers a practical approach for reducing reliance on virgin aggregates in transportation construction. The goal of this study is to investigate the mechanical performance of concrete incorporating coarse RCA obtained from bridge demolition projects in eastern North Carolina and to evaluate its suitability for local nonstructural concrete applications. Aggregate characterization, fresh concrete evaluation, compressive strength testing at 7, 28, and 90 days, and full stress–strain analysis were conducted in accordance with ASTM standards. Three replicate cylinders (4 in. × 8 in./102 mm × 203 mm) were tested per mixture and age. Results indicate that increasing RCA replacement primarily affected density and early-age strength, with a limited influence on long-term compressive strength. Although mixtures with high RCA contents exhibited slightly reduced 7-day strength and lower unit weight, all mixtures exceeded Class B strength requirements specified by the North Carolina Department of Transportation at later ages. Stress–strain analysis showed stable post-peak behavior and no systematic increase in brittleness with RCA content. Mixtures incorporating locally available electric arc furnace steel slag demonstrated additional strength enhancement. These results present systematic relationships among RCA replacement levels, strength development, and deformation behavior under practical processing conditions. The study establishes experimentally grounded insight into the mechanical behavior of transportation-derived recycled aggregates and defines practical performance boundaries for their use in nonstructural transportation concrete, especially in eastern North Carolina infrastructure rehabilitation projects.

Infrastructures doi: 10.3390/infrastructures11040135

Authors: Maria Célia Loss Brandão Lorena Yepes-Bellver Moacir Kripka Élcio Cassimiro Alves

This study aims to present a parametric analysis in the optimization problem for steel-concrete composite cellular beams with steel deck slabs. A comparative analysis was carried out considering two scenarios, namely, (i) in the first scenario, the slab span and applied loads were varied, adopting slab configurations from a manufacturer’s catalog for spans of 10 m to 20 m with a step of 2.5 m; (ii) in the second scenario, the same span and loading conditions were considered; however, slab optimization was performed by introducing reinforcement in order to evaluate the resulting impacts on the structural design. In both analyzed scenarios, the objective function was defined as the composite system’s CO2 emissions. The design constraints were defined based on literature recommendations, and to solve the optimization problem, the Particle Swarm Optimization (PSO) algorithm was also adopted. The results demonstrate that the PSO algorithm was effective in identifying optimal solutions and that the introduction of slab reinforcement, combined with optimal design, led to CO2 emission reductions of up to 25% at the highest load levels analyzed.

Infrastructures doi: 10.3390/infrastructures11040134

Authors: Muhammad Ziad Bacha Mario Lucio Puppio Marco Zucca Mauro Sassu

Dynamic response measurements support bridge monitoring and structural assessment because they are obtainable under operational loading and are sensitive to changes in stiffness, boundary conditions, and mass distribution. This article presents a structured scoping review of dynamic-response-based bridge monitoring and assessment. It covers damage-sensitive indicators, stiffness/capacity proxy inference, interpretation under operational and extreme loading, sensing with acquisition (contact, and indirect/drive-by), and data processing, machine learning and digital-twin integration for decision support. Evidence was identified through targeted searches in Scopus and The Lens with duplicate resolution in Zotero. The cited studies are compiled into a traceable evidence inventory linked to method families and decision objectives. The synthesis shows that global modal properties enable change screening but are highly confounded by environmental/operational variability. Localization and state characterization typically require denser or higher-fidelity sensing and signal conditioning. Finally, capacity-related inference using calibrated conversion models or machine learning (ML) surrogates remains context-bounded and validation-dependent. This review provides an end-to-end pipeline, evidence-maturity rubric, and conservative failure-mode checks with escalation logic that tie SHM outputs to inspection and analysis rather than direct condition declarations for bridge owners. This review is intentionally scoped and does not claim PRISMA-style comprehensiveness.

Infrastructures doi: 10.3390/infrastructures11040133

Authors: Udeme Udo Imoh Daniel Akinmade Majid Movahedi Rad

Crumb rubber-modified asphalt concrete (CMAC) has gained increasing attention as a sustainable pavement material capable of improving mechanical performance while utilizing waste tire resources. This study investigates the rutting resistance and fatigue behavior of CMAC using a combined experimental and mechanistic–empirical modeling approach. Asphalt mixtures containing 0–25% crumb rubber by binder weight were prepared and evaluated through Marshall stability and indirect tensile fatigue tests, whereas Fourier-transform infrared spectroscopy (FTIR) was used to examine binder–rubber interactions. The results indicate that crumb rubber significantly influences both the volumetric and mechanical properties of asphalt mixtures. Mixtures containing 10–15% crumb rubber exhibited optimal performances, achieving up to 36% higher Marshall stability and improved fatigue life compared with conventional asphalt mixtures. FTIR analysis revealed that rubber particle swelling and limited chemical interactions enhanced binder elasticity and improved binder–aggregate compatibility. However, excessive rubber content (≥20%) resulted in reduced stability owing to increased binder absorption and decreased effective binder film thickness. A mechanistic–empirical model incorporating viscoelastic, viscoplastic, and fatigue damage parameters successfully reproduced the experimental trends and identified the same optimal rubber content range. The findings demonstrate that CMAC with a moderate rubber content can enhance pavement durability and structural performance while promoting environmentally sustainable road construction through the reuse of waste tires.

Infrastructures doi: 10.3390/infrastructures11040132

Authors: Hanwen Zhang Myun Kim

Highway landscape quality is important for visual comfort, environmental coordination, and infrastructure management. However, conventional assessment methods rely heavily on manual inspection and qualitative judgment, which are subjective and inefficient for large-scale applications. To address this issue, this study proposes an AI-based quantitative evaluation framework for highway landscape quality using an improved Panoptic-DeepLab model for panoramic image segmentation. The model identifies major landscape elements in highway scenes, including vegetation, sky, roads, buildings, and billboards. Based on the segmentation results, the proportions of natural elements, spatial openness, and artificial interference are integrated into a landscape quality score (LQS) model for quantitative assessment. Experimental results demonstrate that the proposed method achieves reliable segmentation performance and stable convergence in complex highway environments. Comparative analysis further shows that the method provides competitive accuracy with good computational efficiency. The proposed framework offers an effective tool for highway landscape evaluation and can support highway planning, landscape optimization, and visual environment management.

Infrastructures doi: 10.3390/infrastructures11040131

Authors: Nafiaa Abdelmadjid Mohamed Amine Benmebarek Naima Benmebarek

The problem of seepage beneath dams represents a major technical and economic challenge, particularly for countries such as Algeria, where agricultural and industrial development depends heavily on the management of water resources stored in reservoirs. Such seepage can not only cause significant water losses but also jeopardize the stability of the structure, particularly through the piping phenomenon, which poses a risk of sudden failure. Moreover, the evaluation of seepage becomes critical when it exceeds admissible thresholds, thereby requiring the search for solutions to ensure the waterproofing of foundations. Consequently, the design and optimization of devices such as cutoff walls or drainage systems aim to simultaneously reduce three key parameters: the leakage discharge, the uplift pressure, and the downstream hydraulic gradient, in order to guarantee the safety and durability of the infrastructure. The existing literature on cutoff walls beneath concrete dams does not allow for a comprehensive evaluation of the combined effects of geometric and operational parameters. This study aims to address this gap by systematically analyzing the interaction of these factors and their influence on the hydraulic response of the system. Numerical modeling was carried out using the Plaxis 2D software, considering various geometric and parametric configurations. The results indicate that the position, depth, and inclination of the cutoff wall significantly affect the hydraulic performance of the structure.

Infrastructures doi: 10.3390/infrastructures11040130

Authors: Tariq Lamei Ahmed Elsayed Ahmed Ibrahim Ahmed Abdel-Rahim

Multimodal freight transportation networks are increasingly exposed to natural and human-made disruptions, yet prior research remains fragmented in how disruptions are represented, which modeling techniques are applied, and how results are validated, limiting comparability and actionable guidance for resilient planning. This study presents a PRISMA-guided systematic review of disruption modeling in multimodal freight networks. A total of 21 studies were identified and coded to address three research questions concerning (RQ1) which analytical and computational modeling techniques are applied; (RQ2) to what extent models represent cross-modal interdependencies, cascading failures, and recovery processes; and (RQ3) what validation, calibration, and empirical testing strategies are employed. The review shows that optimization-based approaches and hybrid frameworks dominate the literature, complemented by fewer network science and data-driven methods. Most studies model disruptions as node/link failures and/or capacity degradation using static single-event scenarios, and explicit representations of cascading effects, operational delay propagation, and time-evolving recovery trajectories remain relatively rare. While many studies rely on real network data, formal calibration and historical backtesting against observed disruption events are uncommon, and validation is primarily case study-based. These findings highlight the need for more dynamic resilience modeling, stronger uncertainty quantification, standardized reporting of performance and resilience metrics, and greater use of empirically grounded validation to improve the generalizability and decision relevance of multimodal freight resilience models.

Infrastructures doi: 10.3390/infrastructures11040129

Authors: Amirhossein Hassani Borna Abramović Muhammad Shahid Marko Ševrović

Road safety studies commonly use machine learning to predict crashes or to estimate crash-based treatment effects. This study instead audits the modelled fatal-and-serious-injury (FSI) risk produced by the iRAP ViDA risk engine. We analyse 147,466 segments (100 m each) from 12 surveys grouped into four European reporting groups. In Stage 1, gradient-boosted trees reproduce the engine’s risk surface under road-grouped cross-validation(R2 ≈ 0.92 with flows and survey identifiers), and Shapley-based attributions identify which coded attributes drive modelled risk at 396 hotspots (top-three segments per road). In Stage 2, a causal-forest double machine learning estimator adjusts for 38 covariates to estimate segment-level conditional contrasts between modelled risk and six retrofittable treatments across all eligible segments. Simple absolute and relative reduction thresholds translate these associations into 1170 association-based candidate upgrades. On 321 over-lapping hotspots, the candidate upgrades show moderate agreement with iRAP’s Safer Roads Investment Plan (Recall = 0.77; Precision = 0.66; Cohen’s κ = 0.40). All results are conditional associations on a calibrated risk engine whose totals are anchored to project- or network-level fatality totals or fatality estimates used in calibration, not causal effects on observed crashes.

Infrastructures doi: 10.3390/infrastructures11040128

Authors: Giuseppe Campione Michele Fabio Granata

This paper investigates lightweight structural systems based on pultruded GFRP girders for the replacement of deteriorated concrete bridge decks on existing piers and abutments. The study is motivated by the need to rehabilitate short- and medium-span bridges affected by aging deterioration such as reinforcement corrosion. The approach preserves existing piers and foundations and, when required, enables rapid deployment for temporary or emergency applications. The proposed GFRP deck–girder solutions significantly reduce structural mass compared to conventional concrete systems. This reduction leads to lower seismic demand and smaller horizontal forces transmitted to the substructures. The research assesses the structural performance and feasibility of these systems, with particular attention to strength and serviceability behavior. The objective is to identify solutions that can be replicated across different bridge configurations, while also outlining efficient strategies for onsite assembly. After a reasoned review of the solutions available in the literature and of the limitations related to deformability, strength, and instability for a preliminary analytical design approach, three-dimensional numerical simulations of GFRP bridge deck systems are performed to evaluate global behavior and load-transfer mechanisms. The latest design codes and guidelines for GFRP bridges are reviewed and applied. Based on the results, recommendations are provided regarding cross-sectional proportions and member slenderness. The numerical results are compared with the analytical design approach, showing that, under characteristic load combinations, maximum deflections can be limited to approximately L/300–L/400 when the beam depth-to-span ratio range is between 1/10 and 1/6. Within these relationships, spans between 10 m and 25 m are found to be efficient. Additional guidance is proposed for modular construction strategies based on standardized pultruded elements and factory-controlled bonded connections.

Infrastructures doi: 10.3390/infrastructures11040127

Authors: Konstantina Georgouli Christina Plati Andreas Loizos

Permanent deformation, manifested as rutting, remains one of the most critical threats to the structural integrity and functional performance of flexible pavements. The Mechanistic–Empirical Pavement Design Guide (MEPDG) includes rutting models that are highly sensitive to the dynamic modulus (E*) of asphalt mixtures—a parameter that can be determined experimentally or predicted by analytical models. In this study, the influence of E* prediction error on rutting estimation is systematically evaluated by comparing laboratory-measured E* values with those predicted by two models: NCHRP 1-37A and a locally calibrated model. The dynamic pavement behavior and rut depth predictions were determined using the finite layer program 3D-Move under standard traffic loads. Comparative analysis revealed that the NCHRP 1-37A model tends to underestimate E*, leading to significant overestimation of vertical strains and accumulated permanent deformation. In contrast, the locally calibrated model provided predictions that closely matched the laboratory measurements, resulting in minimal deviation in rut depth estimates. The results highlight the importance of local calibration and model selection to improve the reliability of mechanistic–empirical pavement predictions, enabling smarter pavement performance evaluation and supporting more sustainable pavement management practices, especially when laboratory testing is not feasible.

Infrastructures doi: 10.3390/infrastructures11040126

Authors: Nohemí Olivera Juan Manuel Mayoral

The performance of ballasted railway tracks under cyclic loading is a critical issue in urban railway systems, where high traffic frequency and geometric constraints accelerate track degradation, leading to the accumulation of plastic deformations that may reduce operational efficiency. This study presents a numerical framework for rail track performance assessment based on two complementary modeling approaches: a fully continuous Finite Difference Method (FDM) model, and a hybrid Discrete Element Method–Finite Difference Method (DEM–FDM) model. The continuous FDM simulations are employed to evaluate the global mechanical response of the track support system and to compute conventional stability indicators, including the factor of safety (FS). In parallel, the hybrid DEM–FDM simulations explicitly represent the ballast layer using DEM to capture inter-particle interactions, accumulation of permanent deformation, and particle fragmentation under cyclic loading, while rails, sleepers, sub-ballast, and subgrade are modeled using FDM to describe system-level load transfer. Ballast performance is assessed by linking safety factors obtained from the continuous models with mechanically derived permanent deformation and stress measures extracted from the hybrid simulations. The proposed dual-modeling framework enables a systematic investigation of the influence of ballast layer thickness and material type on deformation accumulation, stress transmission, and granular degradation mechanisms. The results reveal distinct behavioral trends among different ballast materials, showing that increased ballast thickness generally improves track performance, while material-specific degradation mechanisms govern the evolution of permanent deformation under repeated loading. The proposed approach establishes a quantitative bridge between traditional stability-based design metrics and deformation-based performance indicators, providing a rational basis for performance-based evaluation, comparison, and optimization of ballast configurations through a set of robust numerically derived relationships for railway track design.

Infrastructures doi: 10.3390/infrastructures11040125

Authors: Antônio Carlos Rodrigues Guimarães William Wilson dos Santos Lucas Marinho Buzatto Caio Vinícius Schlogel Gabriel de Carvalho Nascimento Sergio Neves Monteiro Lisley Madeira Coelho

Structural evaluation of railway tracks in operation requires the integration of field measurements and numerical models capable of adequately representing the mechanical behavior of permanent railway pavement components. In this context, this study presents the structural analysis of a railway segment based on the combination of field instrumentation, laboratory testing, and numerical simulations grounded in the Finite Element Method, adopting linear elastic and resilient material behavior for all track components, using SysTrain software (v.1.88).The objective of this work is to assess the application of a back-analysis methodology based on field instrumentation and numerical modeling, as well as to verify the structural conditions of an in-service railway pavement. The back-analysis was conducted using the SysTrain software, with a focus on calibrating the ballast resilient modulus (RM) and analyzing its effects on the propagation of stresses, internal forces, and displacements throughout the track structure. To this end, field-measured deflections obtained from LVDT sensors installed at the sleeper ends were used, together with the geotechnical, resilient, and permanent deformation (PD) characterization of the underlying soil layers obtained in the laboratory. The results indicated that the calibration of the numerical model requires a ballast resilient modulus in the order of 1500 MPa, suggesting a condition of high layer stiffness. The simulations showed vertical stress levels below 100 kPa in the lower layers, while laboratory tests revealed the high susceptibility of the soils to PD, particularly under moisture variations. It is concluded that the applied methodology enables a consistent assessment of the structural conditions of the track and contributes to a more robust understanding of the ballast response under repeated loading, providing support for railway design, maintenance, and management criteria.

Infrastructures doi: 10.3390/infrastructures11040123

Authors: Vincent Baumann Lucas Adélaïde Pierre Argoul

This paper investigates crowd–structure interaction (CSI) on low-frequency floors during concert events. The findings are based on a full-scale experimental study conducted on a floor prototype designed for a specific infrastructure project. Both the structure and the participants were instrumented while performing various rhythmic activities, such as bouncing and jumping. The study emphasizes the necessity of defining load cases based on the music signal, as its frequency and amplitude may have a variable probability of occurrence. Furthermore, human sensitivity to floor vibrations is examined, with specific comfort thresholds identified for different activities. The core contribution of this work lies in quantifying coordination levels for groups of up to 97 jumping individuals, extending the limited existing literature and refining the definition of jumping crowd actions. Additionally, modal characterization of the unoccupied prototype was performed to evaluate the equivalent damping provided by individuals during standing, walking, bouncing, or jumping. The results demonstrate that while the crowd has a significant impact on the system’s equivalent damping, this effect remains highly variable. Finally, the implications of these findings for structural engineering and design practices are discussed.

Infrastructures doi: 10.3390/infrastructures11040124

Authors: Ivan Villaverde Damien Sallé Marco Antonio Montes-Grova Pablo Jiménez-Cámara Amaia Castelruiz-Aguirre Nicolas Pastorelly Jose Carlos Jimenez Fernandez Irina Stipanovic Sandra Skaric Daniel Rodik

Road maintenance remains one of the most resource-intensive and hazardous operations in infrastructure management. Traditional inspection practices rely heavily on manual labour and discrete procedures, often resulting in limited scalability, operator exposure to traffic hazards, and inefficiencies in data collection. This paper presents a novel automated methodology that integrates Unmanned Aerial Vehicles (UAVs) and autonomous mobile robots (AMRs) to enable automated inspection and measurement of road assets through a digital twin (DT) system. The system leverages data fusion and real-time synchronisation between field agents and a centralised digital twin to monitor the retro-reflectivity of vertical and horizontal signage, detect obstacles and vegetation, and support data-driven maintenance planning. A case study conducted on the Italian highway network demonstrated improvements in operational safety, inspection efficiency, and measurement consistency. The results confirm that the integration of UAVs and AMRs within a digital twin framework can significantly improve sustainability, productivity, and workers’ safety in road maintenance operations.

Infrastructures doi: 10.3390/infrastructures11040122

Authors: Nuno Monteiro Azevedo Maria Luísa Braga Farinha Sérgio Oliveira

In concrete dam foundations, failure mechanisms are primarily influenced by natural rock discontinuities, the dam foundation interface, or weaker strata. This paper proposes a large displacement contact model (LDCM) based on spherical particle/surface interactions, which is computationally more robust and simpler than contact models that adopt the real block polyhedral geometry. To reduce computational costs, whenever possible, the contact interaction is defined in small displacements. The proposed LDCM is applied to a masonry arch under static loading and to the stability analysis of both a gravity dam and an arch dam. The results presented validate the proposed LDCM, and the numerical predictions are close to results obtained experimentally and closely match those obtained with a more complex polyhedral-based model. The advantages of the LDCM are highlighted, namely the decoupling of contact refinement from block refinement, which significantly reduces the computational costs for the masonry arch example. The relevance of adopting a LDCM to predict a physically accepted failure mode is emphasized for dam safety. Finaly, it is shown that the LDCM contact model can be readily adopted to assess the stability of complex dam foundation systems, with reasonable computational running times if a hybrid contact approach is used.