Infrastructures

16 pages, 1771 KB

Open AccessArticle

Metaheuristic Optimization of Surge Protection Device in an Urban Water Distribution Network

by Minsung Kim, Dongwon Ko, Jeongseop Lee, Dahong Kim, Yeun Choi, Bongseog Jung, Hyunjun Kim and Sanghyun Kim

Infrastructures 2026, 11(7), 225; https://doi.org/10.3390/infrastructures11070225 - 30 Jun 2026

Abstract

This study investigates the optimal placement of a surge tank to mitigate pressure fluctuations induced by water hammer in a complex, real-world water distribution network (WDN). A transient-flow numerical model was developed using the Method of Characteristics (MOC) integrated with surge tank theory, [...] Read more.

This study investigates the optimal placement of a surge tank to mitigate pressure fluctuations induced by water hammer in a complex, real-world water distribution network (WDN). A transient-flow numerical model was developed using the Method of Characteristics (MOC) integrated with surge tank theory, applied to a simplified and skeletonized representation of the target network. To determine the most effective installation site, Particle Swarm Optimization (PSO) was employed across 32 candidate nodes. Transient events were simulated through valve closure and reopening operations at the terminal nodes of the network. The results indicate that Node 41 is the optimal location for minimizing head fluctuations. Specifically, the maximum head fluctuation was reduced from 64.32 m in the unprotected system to 51.79 m with the surge tank at Node 41, representing a 19.48% improvement in hydraulic stability. These findings emphasize the critical role of strategic surge tank positioning and provide a robust technical framework for the design and operation of surge protection systems in complex WDNs. Full article

(This article belongs to the Section Sustainable Infrastructures)

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23 pages, 14213 KB

Open AccessArticle

Numerical Investigation of Anti-Floating Punching Failure and Reinforcement Methods for Basement Slabs in High-Rise Structures

by Wenguang Wang, Junqiang Dong, Muzi Zhao and Xin Zhang

Infrastructures 2026, 11(7), 224; https://doi.org/10.3390/infrastructures11070224 - 30 Jun 2026

Abstract

The anti-floating punching failure of basement slabs subjected to groundwater uplift remains insufficiently understood due to the complex stress state and lack of applicable design guidance. This study investigates the punching behavior of a damaged basement slab in Shenzhen, China, using a three-dimensional [...] Read more.

The anti-floating punching failure of basement slabs subjected to groundwater uplift remains insufficiently understood due to the complex stress state and lack of applicable design guidance. This study investigates the punching behavior of a damaged basement slab in Shenzhen, China, using a three-dimensional finite element model developed in LS-DYNA with the Concrete Damage Plasticity (CDP) model. The model was validated against field observations and experimental data, with a prediction error of less than 8%. The results show that anti-floating punching failure evolves from crack initiation in the anchorage zone to damage propagation and final penetration. Increasing the slab thickness from 400 mm to 600 mm significantly alleviated tensile damage concentration and improved stress redistribution. Increasing the concrete compressive strength from 20 MPa to 60 MPa enhanced punching resistance and delayed crack development, but promoted localized brittle failure. Enlarging the foundation pad from CT-6 to CT-9 effectively reduced stress concentration and improved the overall anti-punching performance, whereas the influence of column size was limited. A comparative assessment of three reinforcement measures further revealed their respective applicability under different engineering conditions. The study clarifies the anti-floating punching mechanism of basement slabs and provides a theoretical basis for the anti-floating design and reinforcement optimization of underground structures. Full article

(This article belongs to the Section Infrastructures and Structural Engineering)

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20 pages, 24087 KB

Open AccessArticle

Marker-Assisted Platform Position Measurement Using Forward-View Train Images

by Kodai Matsuoka and Shou Kato

Infrastructures 2026, 11(7), 223; https://doi.org/10.3390/infrastructures11070223 - 29 Jun 2026

Abstract

This study proposes a marker-assisted method for measuring railway platform position using forward-view images captured from in-service trains. Conventional monocular-image-based approaches have limited applicability to precise infrastructure measurement because they suffer from depth-related uncertainty. To mitigate this limitation, the proposed method uses installed [...] Read more.

This study proposes a marker-assisted method for measuring railway platform position using forward-view images captured from in-service trains. Conventional monocular-image-based approaches have limited applicability to precise infrastructure measurement because they suffer from depth-related uncertainty. To mitigate this limitation, the proposed method uses installed ground markers on the platform and sleepers, known marker dimensions, measured installation offsets, and track geometry information. The selected marker reference lines and points define a local transverse measurement plane under near-frontal imaging conditions. The method consists of YOLO-based marker detection, lens-distortion correction, DIC-based marker localization, local pixel-to-metric scale conversion, and vector-based geometric calculation. Field experiments were conducted on an operational regional railway line. When lens-distortion correction and the marker-center-based reference were used, platform gap estimation achieved an MAE of 4.6 mm, an RMSE of 5.3 mm, and a maximum absolute error of 8.8 mm. Platform height estimation improved after lens-distortion correction, with the MAE reduced from 14.2 mm to 9.0 mm, although the maximum absolute error remained 21.2 mm. These results suggest the feasibility of platform gap monitoring under the tested straight-track and near-frontal imaging conditions. Full article

(This article belongs to the Special Issue Advanced Technologies and Approaches for the Condition Monitoring of Railway Transportation Infrastructure)

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33 pages, 8055 KB

Open AccessArticle

An ANP-Weighted Spatial Risk Index for Maritime Traffic Safety in a Marine Protected Tourism Corridor: Evidence from Komodo National Park, Indonesia

by Albertha Lolo Tandung, Antoni Arif Priadi, Sidrotul Muntaha, Meti Kendek, Gassing and Joe Ronald Kurniawan Bokau

Infrastructures 2026, 11(7), 222; https://doi.org/10.3390/infrastructures11070222 - 27 Jun 2026

Abstract

This study addresses maritime traffic risks in the Labuan Bajo–Komodo marine tourism corridor, a spatially constrained archipelagic environment characterized by mixed vessel traffic, intensive tourism activity, and high ecological sensitivity. An integrated decision-support framework was developed by combining the Analytic Network Process (ANP) [...] Read more.

This study addresses maritime traffic risks in the Labuan Bajo–Komodo marine tourism corridor, a spatially constrained archipelagic environment characterized by mixed vessel traffic, intensive tourism activity, and high ecological sensitivity. An integrated decision-support framework was developed by combining the Analytic Network Process (ANP) with stakeholder-supported grid-based spatial risk analysis. Expert pairwise comparisons from eight respondents were used to evaluate eight interdependent criteria: Natural Conditions, Navigational Channel, Vessel Factors, Maritime Traffic Conditions, Port Control, Authority/Stakeholders, Tourism, and Environmental Impact. The ANP calculation was conducted using geometric mean group aggregation, consistency ratio assessment, and targeted follow-up clarification for matrices requiring refinement. The final ANP results show that Port Control received the highest priority weight (0.172), followed by Natural Conditions (0.148), Maritime Traffic Conditions (0.144), Environmental Impact (0.135), Vessel Factors (0.121), Navigational Channel (0.120), Authority/Stakeholders (0.104), and Tourism (0.0566). At the global subcriteria level, communication effectiveness, channel complexity, environmental compliance, local traffic density, and seasonal traffic variation emerged as the dominant contributors to risk. A stakeholder-supported partial spatial risk index (SRI) was then calculated for 21 grid cells using spatially mappable ANP criteria. The highest-risk cells were grids 3, 5, 6, 8, 9, 10, and 14, while sensitivity analysis confirmed that grids 3, 5, 6, 9, 10, and 14 remained high risk across all tested spatial-weight scenarios. The findings indicate that maritime traffic risk in Komodo National Park is not driven by environmental exposure alone, but by the interaction of traffic control capacity, natural hazards, traffic concentration, environmental sensitivity, and institutional coordination. The proposed framework supports spatially informed traffic management, environmental compliance, and emergency preparedness planning in marine protected tourism corridors. Full article

(This article belongs to the Special Issue Data-Driven Innovations in Smart and Safe Transportation Infrastructure)

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28 pages, 8327 KB

Open AccessArticle

Advancing Near-Field Tsunami Fragility Modeling Through Structural Simulation and Post-Event Damage Observations

by Mojtaba Harati and John W. van de Lindt

Infrastructures 2026, 11(7), 221; https://doi.org/10.3390/infrastructures11070221 - 26 Jun 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Earthquake and Multi-Hazard Resilience: Community-Level Insights and AI/ML Applications)

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23 pages, 3384 KB

Open AccessArticle

Research on Effectiveness of Vehicle Driving Simulation System Based on Coupling Modeling of Driving Behavior and Psychology

by Liang Chen, Jialin Yang, Fengbo Liu, Jiming Xie and Mingli Li

Infrastructures 2026, 11(7), 220; https://doi.org/10.3390/infrastructures11070220 - 26 Jun 2026

Abstract

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’ [...] Read more.

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. Full article

(This article belongs to the Special Issue Smart and Safe Pavements: Advanced Techniques for Design, Inspection and Maintenance)

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29 pages, 17373 KB

Open AccessArticle

A Novel Simulation-Based Framework for Predicting Lane-Level Pavement Deterioration Under Freight Loading and Stop-and-Go Urban Traffic

by Nawal Louzi, Mahmoud AlJamal and Mohammad Q. Al-Jamal

Infrastructures 2026, 11(7), 219; https://doi.org/10.3390/infrastructures11070219 - 26 Jun 2026

Abstract

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 [...] Read more.

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/

R^{2}

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. Full article

(This article belongs to the Special Issue Sustainable Road Infrastructure: Safety, Performance and Resilience)

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39 pages, 2158 KB

Open AccessReview

From Flood Hazard to Bridge Decisions Under Uncertainty: A Critical Review of the Scour Monitoring–Prediction–Decision Chain

by Fabrizio Scozzese

Infrastructures 2026, 11(7), 218; https://doi.org/10.3390/infrastructures11070218 - 26 Jun 2026

Abstract

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 [...] Read more.

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. Full article

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32 pages, 31139 KB

Open AccessArticle

Field Performance of a Pile-Cap Ground Improvement System for High-Speed Railway Embankments in Karst Terrain

by Yehia Miky, Mahmoud Abo El-Wafa, Mohamed A. Badran, Hilal Hassan and Ahmed S. Eisa

Infrastructures 2026, 11(7), 217; https://doi.org/10.3390/infrastructures11070217 - 25 Jun 2026

Abstract

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 [...] Read more.

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. Full article

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17 pages, 1701 KB

Open AccessArticle

Uncertainties of Estimating the Conductive Heat Flux at a Pavement Surface

by Chan Huang and Chuanchong Wei

Infrastructures 2026, 11(7), 216; https://doi.org/10.3390/infrastructures11070216 - 24 Jun 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Sustainable Road Infrastructure: Safety, Performance and Resilience)

34 pages, 6525 KB

Open AccessArticle

Traffic Operation Resilience of a Wind-Hazard-Affected, Low-Redundancy Desert Expressway Corridor: Mechanism Identification and Evaluation

by Mengjun Chen, Wuping Ran, Jing Zhang, Long Cheng, Qianqian Qiu, Linkun Jia and Yaohan Su

Infrastructures 2026, 11(7), 215; https://doi.org/10.3390/infrastructures11070215 - 24 Jun 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Next-Generation Transportation Infrastructures: Sustainability, Digitalization, and Resilience)

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31 pages, 1373 KB

Open AccessReview

A Review of Soil–Tool Interactions in Submarine Trenching Operations

by Dinghua Zhang, Yuanyuan Guo, Qingqing Yuan, Hongyang Xu, Zirong Ni, Xiao Liu and Lei Gao

Infrastructures 2026, 11(7), 214; https://doi.org/10.3390/infrastructures11070214 - 24 Jun 2026

Abstract

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 [...] Read more.

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. Full article

14 pages, 1610 KB

Open AccessArticle

An Ensemble Learning-Based Approach to Quantify Post-Earthquake Functional Recovery of a Steel Moment-Resisting Frame Inventory

by Mohsen Zaker Esteghamati and Shiva Baddipalli

Infrastructures 2026, 11(7), 213; https://doi.org/10.3390/infrastructures11070213 - 24 Jun 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Earthquake and Multi-Hazard Resilience: Community-Level Insights and AI/ML Applications)

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24 pages, 5519 KB

Open AccessReview

Material Systems and Applicability Evaluation of Transparent Soil: Toward Transparent Model Testing in Geotechnical Engineering

by Shifu Wang, Changxing Zhang, Biao Xia, Meiqian Wang, Zhiyi Tang and Wei Xu

Infrastructures 2026, 11(7), 212; https://doi.org/10.3390/infrastructures11070212 - 24 Jun 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Advanced Sensing Technologies and Smart Construction Materials for Structural Health Monitoring)

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23 pages, 14595 KB

Open AccessArticle

Numerical Investigation of Interaction Behavior in Large-Diameter Buried Parallel Pipelines Subjected to Variations in Internal Conditions

by Jinhong Yu, Hongyue Liu, Manyu Wang, Yingen Shi, Xiangmin Yu, Jinfeng Xu and Jiahao Zhan

Infrastructures 2026, 11(7), 211; https://doi.org/10.3390/infrastructures11070211 - 23 Jun 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Infrastructures and Structural Engineering)

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41 pages, 2261 KB

Open AccessReview

Embodied Carbon in Ghanaian Low-Volume Road Infrastructure: A PRISMA-Guided Systematic Review and First-Pass A1–A3 Scenario Modelling Study

by Obiri Gyadu-Asiedu, Simon Ofori Ametepey, Clinton Aigbavboa, Hutton Addy and Nana Akua Asabea Gyadu-Asiedu

Infrastructures 2026, 11(7), 210; https://doi.org/10.3390/infrastructures11070210 - 23 Jun 2026

Abstract

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 [...] Read more.

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. Full article

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20 pages, 18560 KB

Open AccessArticle

Analysis of Condensation Phenomena in a Long Subsea Road Tunnel in Korea and Development of the Condensation Prediction Diagram

by Hyogyu Kim and Chang-Woo Lee

Infrastructures 2026, 11(6), 209; https://doi.org/10.3390/infrastructures11060209 - 19 Jun 2026

Abstract

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 [...] Read more.

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. Full article

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16 pages, 8344 KB

Open AccessArticle

Analysis of the Ability of Well-Point Dewatering to Inhibit Silty Subgrade Frost Heave

by Tianxiao Tang, Ke Wang, Xin Liu, Yunxi Han and Lin Wang

Infrastructures 2026, 11(6), 208; https://doi.org/10.3390/infrastructures11060208 - 18 Jun 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Infrastructures and Structural Engineering)

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18 pages, 5048 KB

Open AccessArticle

AI-Driven Pavement Condition Assessment from Dash-Cam Imagery: A Comparative Analysis of YOLOv8-Based PCI Estimation, Manual Inspections, and Automated PASER Ratings in Urban Networks

by Giulia Del Serrone, Giuseppe Loprencipe and Laura Moretti

Infrastructures 2026, 11(6), 207; https://doi.org/10.3390/infrastructures11060207 - 18 Jun 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Smart Mobility and Transportation Infrastructure)

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