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

Infrastructures doi: 10.3390/infrastructures11040121

Authors: Marakkath Nidhi Praveena Jagatheesan Shimol Philip

Reinforced cement concrete (RCC) water tanks are essential for water storage and distribution facilities in every region. The durability and structural integrity of RCC water tanks are crucial to maintaining an uninterrupted water supply to the surrounding areas. This study evaluates the structural integrity and functionality of a water tank in Karaikal, a coastal region in the Union Territory of Puducherry, India, subject to severe exposure conditions characterized by high humidity and temperature variability. An RCC water tank with a capacity of 10 lakh L in Thirunallar, Karaikal, is considered in this study. The methodology for the condition assessment includes visual inspection, non-destructive testing (NDT), and structural analysis in STAAD PRO software. NDT, including the Schmidt rebound hammer test and ultrasonic pulse velocity (UPV) test, was employed to evaluate the indicative compressive strength and in situ quality of an RCC water tank. The structure was modelled using structural drawings obtained from the Public Works Department, Karaikal. The NDT testing findings were incorporated into the model, and the structure was analyzed. Finally, the induced stress from the STAAD Pro model was compared with the in situ concrete compressive strength to assess the tank’s structural safety. The rebound hammer test results indicate that the in situ compressive strength of the tank’s beams and columns ranges from 12 MPa to 43 MPa, and the STAAD Pro analysis shows induced stresses ranging from 2.42 to 10.59 MPa. The comparison shows that the structure has higher safety margins. Hence, the deterioration observed during the visual inspection was not due to a deficiency in structural strength but rather to durability issues caused by environmental distress. Finally, suitable repair and rehabilitation methods were recommended to mitigate the deterioration based upon NDT measurements and the outputs of the structural analysis.

Infrastructures doi: 10.3390/infrastructures11040120

Authors: Siqi Xiang Jie Zhang Juan Deng Yuchao Xia Xukai Yuan Qixiang Cai

Post-installed rebars are extensively used in the strengthening and rehabilitation of concrete structures, where compressive stresses in the anchorage zone provide transverse pressure and significantly affect bond behavior. However, it remains unclear how different transverse pressure conditions, particularly one-way and two-way transverse pressure, influence the bonding behavior of post-installed rebars and how their effects differ. To address this gap, this study investigates the effects of one-way and two-way transverse pressure on the bond mechanism and failure mode of post-installed rebars. To achieve this, 22 pull-out tests were carried out under two transverse pressure configurations, namely one-way and two-way transverse pressure, with pressure levels ranging from 0 to 12 MPa. The results show that, without confinement, concrete splitting was the dominant failure mode, whereas under transverse pressure, failure shifted to adhesive failure or adhesive–rebar interface failure. Transverse pressure significantly improved bond strength, with maximum increases of 49.9% under one-way transverse pressure and 82.9% under two-way transverse pressure. Both the transverse pressure configuration and pressure level had a significant influence on failure evolution and bond performance. In general, increasing the pressure level enhanced the interfacial bonding capacity; however, one-way transverse pressure tended to induce stress concentration in the adhesive layer, thereby promoting adhesive-related failure. These findings clarify the role of transverse pressure conditions in the anchorage behavior of post-installed rebars and provide a basis for the design and analysis of post-installed rebar anchorage systems.

Infrastructures doi: 10.3390/infrastructures11040119

Authors: Chang Wang Xiaojun Li Mianshui Rong Xiaoyan Sun Weiqing Meng

Accurate prediction of surface rupture induced by seismogenic fault displacement is essential for the seismic safety assessment of major engineering projects. Most existing numerical simulations adopt quasi-static approaches, in which the effect of fault displacement is simplified as static loading. As a result, these methods cannot represent the dynamic characteristics of the fault rupture process, such as stress-wave propagation, soil inertial effects, and the influence of dynamic loading paths on rupture extension in soil layers. To address this issue, a full-process simulation method is established for simulating rupture of overlying soil subjected to dynamic fault displacement: Firstly, a non-uniform dynamic fault displacement loading is formulated for the two sides of the fault based on viscoelastic artificial boundaries, allowing the differential motion of the bedrock on both sides of the fault to be represented. Secondly, an improved dynamic skeleton curve constitutive model of soil is developed by introducing a minimum modulus constraint, providing an improved description of soil nonlinear dynamic behavior from small-strain hysteresis to large-strain shear failure. The reliability of the proposed method is verified through element-level tests and horizontal-site response simulation. As a benchmark, its ability to reproduce key rupture characteristics under quasi-static conditions is also assessed by comparison with classical quasi-static rupture studies. The method is then applied to simulate rupture extension and deformation response of overlying soil under strike-slip fault displacement. The results show that, compared to quasi-static analysis, dynamic fault displacement produces similar cumulative slip for surface rupture initiation and full connection, but induces transient amplification of peak surface displacement and a wider deformation zone with gentler displacement gradients. These findings demonstrate the necessity of considering dynamic fault dislocation of bedrock–overlying soil interaction in seismic assessments of engineering projects crossing active faults.

Infrastructures doi: 10.3390/infrastructures11040118

Authors: Wei-Chao Hou Yu Xin Ding-Tang Wang Zuo-Cai Wang Zong-Zu Liu

In this study, variational Bayesian inference (VBI) with Gaussian mixture models is applied to update models of nonlinear structures, and then, the calibrated model is employed to estimate the failure probability of structures using a subset simulation (SS) algorithm. To improve the computation efficiency of probabilistic nonlinear model updating, a Gaussian Process (GP) model is used to construct a surrogate likelihood function in Bayesian inference using an active learning algorithm, and then, Gaussian mixture models (GMMs) are employed to approximate the unknown posterior probabilistic density functions (PDFs) of model parameters. The optimized hyperparameters of GMMs can be obtained by maximizing the evidence lower bound (ELBO), and the stochastic gradient search method is used to solve this optimization problem. Based on the optimized hyperparameters, the posterior distributions of model parameters can be approximated using a combination of multiple Gaussian components. Subsequently, the SS algorithm is used to calculate the earthquake-induced failure probability of structures based on the calibrated nonlinear model. To verify the feasibility and effectiveness of the proposed method, a numerical simulation of a two-span bridge structure subjected to seismic excitations was developed. Moreover, the proposed strategy is further applied to estimate the failure probability of a scaled monolithic column structure subjected to bi-directional earthquake excitations. Both numerical and experimental results indicate that the proposed method is feasible and effective for probabilistic nonlinear model updates, and the updated model can significantly enhance the accuracy of structural failure probability predictions.

Infrastructures doi: 10.3390/infrastructures11040116

Authors: Stefan Sedivy Lubos Remek Matus Kozel Juraj Sramek Jan Mikolaj

Modern road infrastructure asset management faces increasing pressure to improve the quality of decision-making processes, also due to limited public resources. The field of road diagnostics is no exception. The aim of the research is to analyze, through a literature review, the possibilities of applying the theoretical concept of information value. The selected point of interest is the tasks associated with the selection of specific sections intended for inspection, monitoring the level of information gain that this inspection can bring. Methodologically, the research is based on a systematic bibliometric analysis of the literature from the Web of Science and SCOPUS databases for the period January 2010 to June 2025. This is supplemented by a non-systematic content review, while the identified publications were processed by the Bibliometrix and VOSviewer tools and subsequently qualitatively interpreted. The result of the research is a synthesis of knowledge from the finally analyzed set of relevant scientific papers. The findings point to a growing interest in linking the process of planning and performing road infrastructure diagnostics with asset management decision-making processes. At the same time, they point to the development of data-oriented and digital approaches, as well as the limited application of the concept of information value in planning inspections before their implementation. The findings indicate that the assessment of expected information benefit represents a promising tool for reducing uncertainty, determining priorities, and allocating resources more efficiently, while its implementation in road infrastructure management requires further methodological research and practical verification.

Infrastructures doi: 10.3390/infrastructures11040117

Authors: Kiriakos Amiridis Nikiforos Stamatiadis Stergios Mavromatis Antonios Kontizas Vassilios Matragos Antonios E. Trakakis

This study investigates the use of three-dimensional (3D) roadway surface-based geometric indicators in traffic crash analysis, with the objective of evaluating their potential to represent the combined effects of highway alignment features more effectively than traditional two-dimensional (2D) indicators. The roadway surface is modeled as a continuous 3D B-spline surface, from which surface-based geometric metrics derived from differential geometry—specifically Gaussian curvature and mean curvature—are calculated. The roadway is segmented into fixed-length surface patches, and crashes are spatially allocated to these patches using a point-in-polygon approach. Patch-level crash frequencies are analyzed using negative binomial regression models, with traffic exposure accounted for through annual average daily traffic (AADT). The results demonstrate that surface-based 3D curvature metrics are statistically significant explanatory variables in crash frequency modeling and are capable of capturing geometric interactions that are not explicitly represented by conventional 2D alignment measures. The proposed framework provides a proof-of-concept for incorporating 3D roadway geometry into highway safety analysis and offers a foundation for future development of integrated, surface-based crash prediction models.

Infrastructures doi: 10.3390/infrastructures11040115

Authors: Ahmad Al-Khreisat Hany A. Abdalla Mu’tasime Abdel-Jaber

This study investigates the shear performance of reinforced concrete (RC) beams strengthened with near-surface-mounted (NSM) carbon fiber-reinforced polymer (CFRP) ropes under ambient and elevated temperature conditions. An experimental program comprising twelve RC beams was conducted, including both normal- and high-strength concrete specimens. The beams were strengthened using CFRP ropes installed at two orientations (45° and 90°) and two spacing configurations (150 mm and 200 mm). Ten specimens were exposed to a temperature of 600 °C prior to shear testing. The experimental results were evaluated against finite element (FE) simulations and shear strength predictions obtained from ACI 440.2R provisions. The FE models demonstrated close agreement with the observed experimental response, whereas ACI 440.2R consistently yielded conservative shear strength estimates, particularly for high-strength concrete beams. The results confirm that inclined CFRP configurations and reduced rope spacing significantly enhance shear capacity, even after severe thermal exposure, with measured strength gains reaching approximately 75% relative to unheated control beams and up to 135% compared to heated control specimen. The findings emphasize the sensitivity of NSM CFRP in terms of strengthening effectiveness to elevated temperature and highlight the limitations of existing design provisions when applied to fire-damaged RC members.

Infrastructures doi: 10.3390/infrastructures11040114

Authors: Yun Sheng Wei Huang Xuedong Fang Yuxing Liu

This study explores the structural safety, mechanical response and optimal construction parameters of the Horizontal Directional Drilling (HDD) technology applied in airport rigid pavements novelly for navigation lighting renovation. This study adopts a combined research method of three-dimensional finite element modeling (FEM) and field tests (full-scale 4C and 4E class airport runway sections). The reliability of the model is verified by the measured data using a Heavy Weight Deflectometer (HWD). The effects of drilling depth, drilling position and typical aircraft loads on the stress and deformation at the bottom of the pavement slab are systematically analyzed. Then, drilling, grouting and non-destructive testing are carried out in the field full-scale test section to investigate the change in pavement bearing capacities. The results show that minimized influence on the mechanical properties of the pavement can be achieved by using 15 cm drilling depths at either slab center or joints. The pavement stiffness slightly decreases by a maximum of 18.9% after drilling. According to the field grouting test, the Impulse Stiffness Modulus (ISM) of most measuring points can be recovered to the original level before drilling. The use of a 10 cm diameter HDD driller meets the structural safety requirements of airport pavements. The HDD technology induces minimized pavement damage and influence on the bearing capacity of the airport runway structure compared with traditional construction technologies, highlighting its advantages in airfield navigation lighting renovations.

Infrastructures doi: 10.3390/infrastructures11040113

Authors: Rauan Lukpanov Lyailya Kabdyrova Duman Dyussembinov Denis Tsigulyov

This study presents the development and experimental evaluation of an impregnation composition for cement concrete pavements aimed at improving ice-phobic performance while preserving tire–pavement adhesion characteristics. The formulation is based on a combination of keratin-containing raw materials and water-soluble polymer components. Optimization showed that a polymer concentration of 2.5% reduces concrete water absorption by 49–53% compared with untreated specimens. Freezing tests conducted at temperatures of 0 to −5 °C demonstrated an additional reduction in water absorption of treated specimens by 33–40% relative to uncoated concrete and improved resistance to ice formation. The influence of the impregnation on tire–pavement interaction was assessed using a direct shear method, revealing minor changes in friction coefficients of up to ~6% for polished and less than 1% for rough surfaces, remaining within acceptable safety limits. Wear resistance was evaluated through rolling tests with model vehicle wheels, where laboratory abrasion occurred after several thousand loading cycles, while probabilistic correction accounting for trajectory variability indicated an extension of service life to the order of tens of thousands of vehicle passes. The results confirm the potential of the keratin–polymer impregnation as an effective approach for enhancing the durability and operational safety of concrete pavements in cold climates.

Infrastructures doi: 10.3390/infrastructures11040112

Authors: Hesham Fawzy Shaaban Ayman El-Zohairy Mohamed Atabi

This study investigates the structural performance of a novel composite space truss deck system as an alternative to the conventional steel box girder in cable-stayed bridges. Using the Suez Canal Bridge as a benchmark, comprehensive linear and nonlinear finite element analyses were performed to evaluate the global behavior of both deck configurations under dead, live, wind, and temperature loads. The proposed system consists of a three-dimensional square-on-square truss acting compositely with a 25 cm reinforced concrete slab, designed to optimize stiffness and material efficiency. The results revealed that the composite space truss deck achieved a 5–7% reduction in mid-span deflection under live loading and a 6% increase in torsional rigidity compared with the steel box girder, while maintaining comparable self-weight (490 kg/m2 versus 480 kg/m2). The influence of geometric nonlinearity was moderate, 6.56% for the space truss and 1.64% for the box girder, whereas temperature variations of ±30 °C induced up to a 25.3% change in mid-span deflection, highlighting the space truss’s higher thermal sensitivity. Parametric analyses demonstrated that increasing the truss depth from 2.5 m to 4.0 m enhanced global stiffness by 15%, and using lightweight concrete reduced mid-span deflection by 30%. Overall, the composite space truss system offers superior stiffness-to-weight efficiency, substantial steel savings (two-thirds less), and competitive construction economy, establishing it as a promising solution for medium- and long-span cable-stayed bridges.

Infrastructures doi: 10.3390/infrastructures11040111

Authors: Julián Pulecio-Díaz Yelena Hernández-Atencia

Roller-compacted concrete (RCC) is a promising alternative to conventional pavement systems due to its structural capacity, rapid construction, and potential for sustainable performance. Nevertheless, its global adoption remains limited by the absence of standardized design protocols, variability in construction practices, and insufficient long-term performance assessments. This study provides a comprehensive and critical review of 125 peer-reviewed publications published between 1967 and 2025, proposing a multi-dimensional integration framework that connects material fundamentals, structural design principles, construction practices, in-service monitoring strategies, and documented applications within a unified analytical perspective. Unlike earlier reviews that addressed these aspects separately, this study explicitly articulates their interdependencies and identifies a fragmented global implementation of RCC monitoring practices, with limited integration of structural, functional, and instrumentation-based assessments across life-cycle stages. The findings consolidate a structured reference framework that supports more consistent, data-driven, and sustainability-oriented use of RCC pavements in contemporary infrastructure projects.

Infrastructures doi: 10.3390/infrastructures11040110

Authors: Cheng Peng Junjie Bi Dongxing Wang Bo Deng

To address the need for enhanced geotechnical performance in gravelly soil stabilization, this study investigated the synergistic effects of guar gum as an additive in enzyme-induced calcium carbonate precipitation (EICP) treatment. Through systematic experimentation combining unconfined compressive strength (UCS) tests, carbonate content quantification, and triaxial analysis, the mechanical behavior of treated soils was evaluated under varying EICP solution concentrations (0–2 mol/L) and curing durations. Results demonstrated that a 1.5 mol/L EICP solution achieved peak strength and carbonate precipitation before subsequent decline, while a 1% guar gum dosage optimized mechanical properties by balancing initial strength enhancement and precipitation efficiency. Scanning electron microscopy revealed microstructural mechanisms wherein guar gum provided heterogeneous nucleation sites for calcite crystals, while its interaction with EICP enabled dual-phase pore filling and interparticle bonding. This synergistic effect created a three-dimensionally reinforced matrix, significantly improving both UCS and unconsolidated undrained shear strength compared to native and EICP-only specimens. The findings establish a theoretical framework for regulating calcite precipitation patterns and enhancing cementation mechanisms in gravelly soil improvement, offering practical guidelines for foundation engineering applications through the combined use of guar gum and EICP.

Infrastructures doi: 10.3390/infrastructures11030109

Authors: Mehdi Ebadi-Jamkhaneh Mohammad Ali Arjomand Mohsen Bagheri Habib Akbarzadeh Bengar Seyed Zeyd Mohammadi Ghalesari

This study investigates the seismic response of reinforced soil retaining walls through reduced-scale 1 g shaking table experiments, with particular emphasis on deformation behavior and pore water pressure generation in saturated sandy soils. Physical models were constructed using Firuzkuh silty sand and extensible fabric reinforcement, considering two soil conditions: an undisturbed loose state and a compacted state with a relative density of 35%. Horizontal dynamic loading with peak acceleration ranging from 1 g to 3 g was applied, while acceleration, displacement, and pore water pressure responses were continuously monitored. The results demonstrate a pronounced depth-dependent pore water pressure response, with deeper soil layers exhibiting higher magnitudes and longer persistence of excess pore pressures. In the undisturbed loose sand, the excess pore water pressure ratio approached unity at depth, indicating near-liquefaction conditions. In contrast, moderate densification significantly reduced pore pressure buildup and promoted partial dissipation during shaking. Reinforcement and compaction were found to effectively limit lateral displacement and settlement, leading to improved seismic performance. The findings highlight the critical roles of soil fabric, density, and reinforcement in controlling deformation and liquefaction susceptibility of reinforced soil retaining walls under seismic loading.

Infrastructures doi: 10.3390/infrastructures11030108

Authors: Shiqi Xie Peng Lu Wenke Yan Shengxu Wang Yi Lu Jinpeng Zhu Mulian Zheng

To address the current absence of targeted gradation design for porous asphalt pavements both domestically and internationally, this study employs the Coarse Aggregate Void Filling (CAVF) method to design the gradation of porous asphalt mixtures. Marshall stability tests, rutting tests, and scattering tests were conducted to investigate the relationship between coarse aggregate proportions and the structural stability of the mixture skeleton. An orthogonal experimental design was further utilized to examine the influence of three levels of fine aggregate gradation on the acoustic absorption characteristics of the mixture, and to analyze the effects of aggregate gradation on the primary pore diameter, connected pore diameter, and connected pore length. The results indicate that the coarse aggregate gradation predominantly governs the skeleton strength and overall pavement performance of the mixture, whereas the fine aggregate gradation exhibits significant effects on the interconnected void ratio, pore structure, and sound absorption performance. The optimal roughness range of coarse aggregates in porous asphalt mixtures is determined to be 0.46–0.52. The proportion of 0.6–1.18 mm aggregates has a pronounced influence on the primary pore diameter, connected pore diameter, and connected pore length. By integrating the design considerations for both coarse and fine aggregate gradations, a recommended gradation range for porous asphalt mixtures is proposed that achieves a balance between pavement performance and sound absorption/noise-reduction effectiveness.

Infrastructures doi: 10.3390/infrastructures11030107

Authors: Wafa Elias Moamar Abu Ahmad Michael Frid

This study investigates how asphalt mixture type influences the degradation of pavement-marking retroreflectivity and luminance contrast under real operational conditions on Israeli intercity roads. Field measurements were collected along 65.1 km of roadway constructed with three asphalt mixtures: basalt dense-graded concrete (Basalt DCG), basalt stone mastic asphalt (Basalt SMA), and basalt–dolomite dense-graded concrete (Zebra DCG). Linear degradation models provided the best representation of retroreflectivity decay (R2 = 0.63). Results show that asphalt mixture type significantly affects initial retroreflectivity, contrast, and effective service life of left-side white paint markings. Markings applied on Basalt DCG exhibited initial retroreflectivity values up to 1.6–1.9 times higher and maintained acceptable visibility for approximately 7–8 months, compared with about 3 months on Zebra DCG under comparable conditions. Traffic volume was not a statistically significant predictor, indicating that degradation is dominated by time-dependent material and optical aging processes. Pavement background reflectivity and its evolution play a critical role in contrast degradation. The results demonstrate that asphalt mixture selection can reduce repainting frequency by approximately 10–15%, highlighting asphalt mixture choice as a practical and previously underrecognized lever for improving pavement-marking durability and long-term visibility.

Infrastructures doi: 10.3390/infrastructures11030106

Authors: Konstantinos Gkyrtis George Botzoris Alexandros Kokkalis

Young drivers are often involved in speed-related crashes, particularly on rural and highway roads. This is usually due to high speeds, unstable control of vehicle positioning, complex road designs, and limited visibility. This study explores how young drivers select their speed and position their vehicle on different types of roads under daytime and nighttime conditions using a driving simulator. Thirty civil engineering students aged 18 to 24 participated in four simulated scenarios: a rural road during the day, rural road at night, highway during the day, and highway at night. They also completed a structured questionnaire about their driving experience, confidence, and perception of risk. Vehicle speed, lateral position, and acceleration were analyzed using descriptive statistics and linear regression. The results indicate that driving on highways resulted in higher speeds and increased lateral wander. Additionally, driver experience and familiarity with the road affected speed choice and vehicle position. Compliance with speed limits was linked to more consistent lane positioning. These findings give important insights into the behavior of young drivers and may suggest ways to improve infrastructure design, visibility, and speed management strategies, thereby helping to reduce crash risk.

Infrastructures doi: 10.3390/infrastructures11030105

Authors: Urszula Kwast-Kotlarek Mariusz Szóstak

The article presents an integrated RACI–AHP–BIM methodology that supports responsibility management, decision-making, and information management in complex construction projects delivered under the design–build model, with particular emphasis on conservation-orientated investments. The approach combines three complementary components: the RACI responsibility matrix, the analytic hierarchy process (AHP), and building information modeling (BIM). The methodology is validated on a higher-education conservation project using a BIM execution plan (BEP), scan-to-BIM procedures, and structured decision-making. The integration of RACI with BIM reduced accountability gaps and improved stakeholder coordination, while linking AHP with BIM data enabled data-driven design decisions using the BOCR model. The findings demonstrate measurable benefits, including clearer responsibility allocation, improved interdisciplinary coordination, and more transparent decision-making. The application of laser scanning and scan-to-BIM supported the creation of a digital model of historic elements for both design and future facility management. The main contribution is a holistic integration of RACI, AHP, and BIM into a unified methodology for conservation-orientated projects with high functional complexity, providing a reference framework for public-sector investment management.

Infrastructures doi: 10.3390/infrastructures11030104

Authors: Gulbarshin K. Shambilova Saule Bukanova Zhanar Kadasheva Nagima Karabassova Mikhail S. Kuzin Igor V. Gumennyi Ivan Yu. Skvortsov Igor S. Makarov

The development of durable road sealing materials capable of maintaining performance under combined mechanical and climatic loads remains a critical challenge for modern infrastructure. Conventional bitumen-based sealants exhibit limited resistance to high-temperature deformation, cracking, and adhesion degradation, leading to reduced service life. This study proposes a rheology-oriented approach to the design of polymer-reinforced bituminous sealants based on penetration-grade bitumen 50/70 and 70/100 modified with styrene–butadiene–styrene (SBS) copolymers up to 9 wt.% and reinforced with cellulose fibers. The rheological behavior of the developed composites was investigated using dynamic shear rheometry to determine the complex shear modulus (G*), phase angle (δ), and temperature–frequency dependencies in the range from −20 to +90 °C, while infrared spectroscopy was employed to assess intermolecular interactions. Adhesion performance was evaluated at different temperature. The modified systems demonstrated a 5–10-fold increase in G*/sinδ enhanced high-temperature stability, and improved adhesion and crack resistance compared to base bitumen. Based on the obtained rheological and performance indicators, the developed composition was approved for subsequent pilot-scale testing and field validation as a promising road sealing material.

Infrastructures doi: 10.3390/infrastructures11030103

Authors: Edoardo Bocci Carlo Carpani

Nowadays, the most widespread solutions to increase the sustainability of bituminous mixes deal with the recycling of reclaimed asphalt pavement (RAP) and the use of warm mix asphalt (WMA). However, the possibility of combining RAP recycling and WMA technologies needs to be further investigated and validated. This comprehensive laboratory study aimed at assessing the feasibility of recycling RAP in WMA mixes without compromising performance. For this purpose, WMA containing 40% RAP was produced by using softer virgin bitumen (160/220), to compensate for the high stiffness and viscosity of the RAP binder, and a WMA chemical additive. The mix was designed and characterized in terms of indirect tensile strength, water sensitivity, complex modulus, resistance to low-temperature cracking, resistance to rutting at high temperatures, and fatigue resistance. Its mechanical properties were compared with those of ordinary HMA made with virgin bitumen (50/70) and aggregates. The experimental results showed that the WMA+RAP mix had comparable volumetric properties with respect to the reference HMA despite its reduced production temperatures. Moreover, WMA+RAP exhibited similar or improved mechanical performance, with enhanced resistance to water damage, rutting, and fatigue cracking, without penalizing low-temperature behavior.

Infrastructures doi: 10.3390/infrastructures11030102

Authors: Hade Dorst Suzan van Kempen Agnieszka Bigaj-van Vliet

We argue that mainstreaming Nature-based Solutions (NbS) requires alignment of diverse value systems and integrated, cross-sectoral collaboration, and we present the necessary conditions for increasing practical implementation. NbS are increasingly recognised as effective strategies to protect critical infrastructures against climate change impacts while enhancing them by delivering ecological, social, and economic benefits. Despite growing policy support, the integration of NbS into mainstream infrastructure planning remains limited due to siloed responsibilities and decision making, entrenched institutional structures that favour grey infrastructure, and challenges in balancing short-term risks with long-term value. We examine if and how NbS mainstreaming in the infrastructure sector could be enabled. Building on insights into infrastructure governance and innovation mainstreaming, we explore perceptions and engagement with NbS and opportunities for strengthening co-governance and collaborative decision making in the Dutch infrastructure domain. A critical insight is that NbS must be understood as part of a broader socio-ecological–technical system rather than isolated interventions. This results in requirements for more integrated approaches to governance and planning as well as assessment. Asset managers in particular could play a pivotal role by adopting holistic performance assessments that consider co-benefits and trade-offs.

Infrastructures doi: 10.3390/infrastructures11030101

Authors: Raul Almeida Adriana Santos Susana Faria Elisabete Freitas

Surface texture plays a key role in pavement safety and performance, yet its degradation is influenced by multiple interacting factors that vary across road networks. This study developed statistical models to characterize and predict surface texture evolution on Portuguese highways using linear mixed-effects modeling. Texture measurements collected on 7204 pavement sections, each 100 m in length, over three monitoring cycles were analyzed alongside traffic, climatic, pavement structural, geometric, and spatial variables. The hierarchical structure of the data, with repeated measurements nested within pavement sections, was explicitly accounted for via random intercepts and random slopes. At the same time, temporal correlation was modeled via an autoregressive error structure. Two model specifications were evaluated: a model including only traffic and climatic variables and an extended model incorporating pavement and geometric characteristics. Results indicate that texture evolution is statistically associated with cumulative traffic loading, temperature-related indicators, precipitation, surface course type, lane position, vertical alignment, and altitude. The extended model showed a significantly better fit and superior predictive performance, as confirmed by information criteria and cross-validation metrics. The findings highlight the importance of accounting for section-level heterogeneity and roadway characteristics when modeling texture degradation. The proposed modeling framework provides a statistically scalable and robust tool for texture prediction, accounting for regional-specificities and long-term pavement management decisions.

Infrastructures doi: 10.3390/infrastructures11030100

Authors: Qiwei Lin Yujing Jiang Satoshi Sugimoto

Tunnel linings are critical structural components of underground infrastructure, and their seismic performance plays a decisive role in maintaining the serviceability and safety of tunnels. Under dynamic loading, excessive deformation and damage of the lining may reduce the effective cross-sectional capacity and threaten the minimum safety clearance required for tunnel operation. Therefore, it is essential to investigate the deformation behavior and failure mechanisms of tunnel linings subjected to seismic excitation and to evaluate the effectiveness of reinforcement measures. In this study, a coupled numerical framework combining the finite difference method (FLAC3D) and the discrete element method (PFC3D) is developed to analyze the dynamic response of tunnel lining systems. The surrounding rock mass is modeled in FLAC3D to simulate stress wave propagation and global deformation, while the tunnel lining is represented in PFC3D using bonded particles to capture crack initiation, propagation, and post-peak failure behavior. The proposed FLAC3D–PFC3D coupled approach provides an effective tool for evaluating the seismic performance of reinforced tunnel linings and offers a practical basis for the design and assessment of seismic strengthening measures in underground engineering.

Infrastructures doi: 10.3390/infrastructures11030099

Authors: Huating Chen Jianfei Du

To elucidate the degradation behavior of elastic modulus in normal-strength ordinary concrete under flexural fatigue loading, this study systematically examines its evolution in C50 concrete, which is widely used in engineering applications. Based on four-point bending fatigue test data of plain concrete (PC) and reinforced concrete (RC) beams, degradation curves of the relative residual elastic modulus as a function of the cycle ratio were established. To quantitatively characterize the fatigue degradation process, two integrated indicators—the area under the curve (AUC) and the stable-stage degradation slope (|Kmid|)—were introduced to represent the degree of cumulative damage and the degradation rate of elastic modulus, respectively. These indicators were subsequently employed to evaluate the effects of maximum stress level, stress ratio, and reinforcement on elastic modulus degradation. The results show that failed PC specimens exhibited a typical three-stage S-shaped degradation pattern, whereas RC specimens primarily exhibited a two-stage degradation behavior. However, the elastic modulus of runout PC specimens remained above 93% of its initial value throughout the entire loading process. For PC specimens, under the same maximum stress level, increasing the minimum stress level from 0.10 to 0.25 resulted in a 24% decrease in |Kmid| from 0.2505 to 0.1912. At the same minimum stress level, increasing the maximum stress level from 0.75 to 0.90 led to a 94% increase in |Kmid| from 0.1912 to 0.3705. The presence of reinforcement increased AUC by 3~15% and reduced |Kmid| by 54~74%, indicating that reinforcement not only mitigated overall damage accumulation but also significantly slowed the degradation rate of the elastic modulus during the stable fatigue stage. The degradation characterization approach proposed in this study provides a simplified and practical framework for fatigue analysis of concrete components based on damage mechanics.

Infrastructures doi: 10.3390/infrastructures11030098

Authors: Chi Chen Shenglin Wang Xiaoyuan Li Dengwei Yang

Three-dimensional printed concrete (3DPC) has emerged as an innovative construction technology for extreme environments, offering advantages in thermal insulation, reduced labor requirements, and rapid construction. However, this layer-by-layer deposition process brings interlayer effects that affect mechanical anisotropy, permeability, and thermal performance, posing challenges for structural reliability. This review systematically examines current methods for characterizing and mitigating interlayer effects in 3DPC. Material-related factors—including admixtures, aggregates, recycled materials, fibers, and geopolymer incorporation—alongside process parameters such as printing speed, nozzle geometry, layer height, interlayer time, and environmental conditions, are analyzed for their influence on interlayer quality. State-of-the-art techniques for evaluating interlayer voids, mechanical behavior, and thermal performance are summarized. Moreover, results from micro-imaging, mechanical testing, and heat transfer assessments are also introduced. Ultimately, strategies for optimizing material composition and printing parameters to improve interlayer bonding and overall performance are highlighted. Overall, this paper provides a methodological framework to guide the design, testing, and practical implementation of 3DPC in demanding engineering applications.

Infrastructures doi: 10.3390/infrastructures11030097

Authors: Sajjad Noura Fahd Ben Salem Alan Carter

Cold recycled asphalt mixtures incorporate a high amount of reclaimed asphalt pavement (RAP), which offers more economic and environmental advantages than hot recycling techniques. Nevertheless, the presence of aged RAP binder frequently leads to reduced low-temperature performance and uncertainty in mechanical response. The influence of slack wax on full-depth reclamation (FDR) mixtures with bitumen emulsion is assessed in this study using a dual-scale approach. The approach integrates both chemical and rheological binder-scale characterization with mixture-scale mechanical performance with variability assessment. At the binder scale, the binder beam rheometer (BBR), dynamic shear rheometer (DSR), and Fourier transform spectroscopy (FTIR) indicated that the addition of 10% recycling agent improved the low-temperature properties. The improvement at lower temperatures shifted the BBR temperature from −23 °C to −30 °C, which ultimately resulted in a less negative ΔTc, from −0.7 °C to −0.3 °C, and moderately improved high-temperature stiffness. Moreover, the FTIR analysis indicated a reduction in oxidation-related chemical markers, as evidenced by the reduced carbonyl and sulfoxide indices. At the mixture scale, complex modulus shows a systematic decrease in stiffness, particularly at lower temperatures of −25 °C and −15 °C, and a reduced phase angle, suggesting higher elastic dominance. The reduction is observed at all temperatures and frequencies. Rutting resistance of both formulations remains below 3% after 30,000 cycles. The complex modulus coefficient of variability was found to be 8–12%, comparable to that of hot mix asphalt. In conclusion, the findings suggest that the recycling agent provides a controlled restoration of viscoelastic properties in cold recycled mixtures without compromising structural integrity. This underscores the significance of multi-scale evaluation and variability assessment when characterizing high RAP recycling agents under the studied materials and dosage.

Infrastructures doi: 10.3390/infrastructures11030096

Authors: Shuaixin Yang Jiejun Huang Nan Li Han Zhou Hua Li Xiaoguang Zhang Xinping Li

In large-scale dam construction, the efficiency of concrete transport operations is fundamentally governed by the coordination between horizontal hauling and vertical hoisting capacities. Traditional experience-based scheduling approaches often fail to capture the stochastic, cyclic, and resource-coupled nature of these transport systems. This study developed a closed queuing network-based stochastic simulation framework to model dam concrete transportation as a finite-population cyclic service system. The process was abstracted into sequential service stages with stochastic service times, and a structured state-space representation combined with time-step simulation was constructed to describe dynamic resource occupation and task transitions under varying truck and cable crane configurations. Application to a real large-scale dam project revealed a characteristic multi-stage performance evolution pattern governed by capacity matching mechanisms. As the truck fleet size increased, system performance transitioned from a transport-limited regime to a capacity-coordination regime and ultimately to a hoisting-saturated regime in which further fleet expansion yielded diminishing returns. Sensitivity analysis demonstrated that hoisting capacity imposed an upper bound on system throughput, while adaptive fleet reconfiguration could restore operational equilibrium under constrained equipment availability. The results indicated that dam concrete transport should be treated as a dynamic capacity regulation problem rather than a static allocation task. The proposed framework provides an interpretable and quantitative decision-support tool for equipment configuration, bottleneck identification, and adaptive scheduling in large-scale hydraulic infrastructure projects.

Infrastructures doi: 10.3390/infrastructures11030095

Authors: Horia Ameen Natchapon Jongwiriyanurak Jesús Balado Mario Soilan

Road safety assessment is essential for reducing traffic fatalities, with road infrastructure contributing to a substantial proportion of crashes worldwide. International frameworks such as the International Road Assessment Program (iRAP) define standardized attributes for infrastructure auditing; however, many of these attributes remain challenging to automate using imagery alone. This study evaluates V-RoAst (visual question answering for road assessment), a public dataset of road images that are annotated with iRAP-style attributes, using state-of-the-art multimodal large language models (MLLMs), specifically Gemini 2.0 and Gemini 2.5. The analysis focuses on how prompt design influences the accuracy and stability of single image iRAP inference. A token-efficient reduced prompt is developed that preserves the iRAP schema while removing single-class constants, hard-coded administrative fields, and derived or non-visual codes, retaining only visually interpretable attributes. Performance is compared with the original full multi-attribute prompt and single attribute prompts using a fixed evaluation protocol incorporating majority voting, bootstrap 95% confidence intervals, and per-code sample-size checks. Results indicate only minor performance differences between Gemini 2.0 and Gemini 2.5, while prompt optimization produces the most consistent gains, improving macro-F1 scores and tightening confidence intervals for visually grounded attributes such as roadside severity, intersection channelization, and service-road presence. Token analysis shows an approximate 30% reduction in prompt length, reducing computational cost and truncation risk. Overall, the findings demonstrate that prompt scope has a greater impact than model version in image-only iRAP coding, offering practical guidance for scalable infrastructure assessment.

Infrastructures doi: 10.3390/infrastructures11030094

Authors: Flaviu Ioan Nica Teodor Iftimie

The paper presents the evaluation and research undertaken to propose an optimal solution for the Capra–Bâlea road tunnel, within the framework of rehabilitating and modernizing the entire road section, with the objective of ensuring uninterrupted vehicular traffic during the winter season. The Capra–Bâlea road tunnel is the longest operational and under exploitation tunnel in Romania, measuring 887 m, and the highest-altitude road tunnel structure in the country, at 2042 m above sea level. It serves as a connection between the historic regions of Tara Romaneasca and Transylvania via the DN7C national road, commonly referred to as the Transfagarasan, which is among Romania’s most significant tourist routes, and contains five of the ten existing road tunnels in the country. The tunnel passes through crystalline metamorphic rocks typical of the Fagaras mountains. The construction method was typical of the 1970s, combining drill-and-blast in the central section with cut-and-cover execution at the two ends. The technical condition of the tunnel, evaluated through a detailed technical inspection, is presented, highlighting defects and proposing rehabilitation or restoration solutions. The existing cross sections are described and comparatively analyzed against the currently recommended cross-sections in accordance with present standards and gauge requirements. A three-dimensional simulation of both the current and original cross-sections was performed to investigate the behavior of this type of structure, and solutions for tunnel rehabilitation and modernization are recommended. Finally, the advantages of the proposed solution are discussed.

Infrastructures doi: 10.3390/infrastructures11030093

Authors: Navoda Abeygunawardana Hikaru Nakamura Tatsuya Nakashima Taito Miura

This study numerically examined the anchorage mechanism of rebar hooks under varying straight development lengths, including high stress levels. A three-dimensional rigid body spring model (3D RBSM) was used for the investigation and successfully reproduced the experimental pullout test stress–slip relationships and inner–outer strain distributions for the rebar hook with and without a straight development length. A validated numerical model was used to assess local concrete stresses and internal crack propagation, enabling a clear interpretation of how straight development length influences the anchorage mechanism. The results revealed that increasing straight development length increases stiffness, reduces rebar strains and concrete stresses in the hook region, promotes crack formation around the rebar surface, and forms maximum tensile stresses closer to the top surface, ultimately resulting in earlier splitting failure at high rebar stress levels. A comparison of cases with and without hooks shows that combining the hook with straight development length improves stress distribution, delays crack propagation, and increases anchorage by reducing tensile stress concentrations near the top surface and side faces. These findings provide valuable insights into the role of straight development length in the anchorage performance of 180-degree rebar hooks.

Infrastructures doi: 10.3390/infrastructures11030092

Authors: Mohanad Wisam Mousa Sarmad Shafeeq Abdulqader Ahlam Sader Mohammed

This study presents an experimental investigation into the rehabilitation of fire-damaged reinforced concrete (RC) columns using Ultra-High-Performance Concrete (UHPC) under an eccentric load of (e = 45 mm). The experimental program comprised nine small-scale RC column specimens, which were divided into two groups based on exposure temperatures of 500 °C and 700 °C, applied using a specially designed furnace. A control column that was not exposed to fire was also tested for comparison. The study included two fire exposure durations: 60 and 120 min. During the heating phase, the columns were subjected to a pre-applied axial load equal to 50% of their ultimate capacity (Pu). After sustaining fire-induced damage, the columns were rehabilitated using UHPC jacketing. The experimental results revealed a reduction in the ultimate load-carrying capacity of the RC columns with increasing fire temperature and exposure duration. Specifically, the load capacity decreased by 22.68% and 33.89% when exposed to 500 °C for 60 and 120 min, respectively, and by 42.02% and 49.02% when exposed to 700 °C for 60 and 120 min, respectively, compared with the control column. However, strengthening the fire-damaged columns with UHPC significantly enhanced their structural performance, resulting in an increase in ultimate load capacity ranging from 81.88% to 157.14% compared with their corresponding fire-damaged unstrengthened specimens. Based on the experimental findings, the load lateral displacement response at mid-height, load–axial deformation curves, failure modes, ductility, and stiffness characteristics of the columns were analysed. The study concludes that the use of UHPC in rehabilitating fire-exposed columns substantially improves most of these structural properties.

Infrastructures doi: 10.3390/infrastructures11030091

Authors: Ying Peng Nida Chaimoon Yike Wu Yuanfeng Chen Krit Chaimoon

Reactive powder concrete (RPC) delivers outstanding mechanical performance and durability; however, it is commonly hindered by high cement consumption, elevated embodied carbon emissions, and high material costs. To mitigate these drawbacks, this study develops a low-carbon, cost-effective RPC incorporating high-volume class-F fly ash, a reduced silica fume dosage, conventional river sand, and an optimized steel fiber system. A systematic mix design framework, combining particle packing density with paste rheology optimization, was employed to balance workability, strength, and durability. The optimized mixtures were evaluated for compressive, splitting tensile, and flexural strength, as well as durability-related metrics, including water absorption rate and resistance to chloride penetration. Environmental impact and cost-effectiveness were further quantified via embodied carbon accounting and strength-normalized performance indices. The results show that well-designed high-volume fly ash RPC can achieve compressive strengths above 130 MPa while maintaining excellent impermeability, alongside substantial reductions in both material cost and carbon footprint relative to conventional RPC. In addition, mixed-size steel fibers further enhance mechanical performance through multi-scale crack bridging. Overall, this work provides a practical route to decouple ultra-high performance from high environmental burden, supporting the sustainable deployment of RPC in infrastructure engineering.

Infrastructures doi: 10.3390/infrastructures11030090

Authors: Elia Ferrari Jonas Meyer Stephan Nebiker

The effective management of municipal road infrastructure requires up-to-date, standardized and reliable condition information to support sustainable maintenance. While visual road-condition assessment methods based on established standards are widely applied to municipal roads, they remain largely manual, time-consuming, costly and subjective. This study presents an end-to-end workflow for the automated visual inspection and condition assessment of municipal road infrastructure using high-resolution, 3D street-level imagery acquired by professional mobile mapping systems. The proposed approach integrates an efficient preprocessing pipeline for precise road-surface extraction with deep learning models trained for the specific task and an advanced postprocessing method for robust results aggregation. For this purpose, a large dataset covering approximately 352 km of municipal roads across eight municipalities was created by combining street-level imagery with expert-annotated road-condition index (RCI) values. Two neural network variants were implemented: a regression model predicting standardized RCI values and a binary classifier distinguishing between roads requiring maintenance and those in good condition. To ensure decision-oriented outputs at the infrastructure-asset level, frame-based predictions are aggregated into homogeneous road segments using outlier detection and change-point analysis along the road axis. The regression model achieved a mean absolute error of 0.48 RCI values at frame level and 0.40 RCI values at road-segment level, outperforming conventional inter-expert variability, while the binary classification model reached an F1-score of 0.85. These findings demonstrate that AI-based visual road-condition assessment using professional mobile mapping data can provide accurate, standardized and scalable condition information for municipal road infrastructure. The proposed workflow supports maintenance prioritization and infrastructure management decisions without requiring explicit detection of individual pavement defects, offering a practical pathway toward automated, cost-effective road-condition monitoring.

Infrastructures doi: 10.3390/infrastructures11030089

Authors: Imran Badshah Asad Ali Pan Lu Amin Keramati

Highway–rail grade crossings (HRGCs) are locations where roadways and railway tracks intersect at the same level. Due to the shared level of travel and the substantial mass disparity between trains and highway users, collisions at these crossings tend to be catastrophic. As a result, HRGC crashes represent a major public safety concern in the United States. While previous studies have evaluated contributing factors to crash severity, there has been limited focus on the role of highway users’ action and its influence on crash severity. This study aims to examine all relevant factors, with a particular focus on highway user actions. The dataset, sourced from the Federal Railroad Administration’s database, includes data from six states between 2013 and 2022, specifically addressing severity and contributing factors. The proportional analysis highlights that highway user actions such as “went around the gate”, “did not stop”, and “stopped on the crossing” dominantly contribute to crash severity. A multinomial logistic regression was employed to identify significant determinants of crash severity. Odds ratio analysis reveals that “went around the gate” significantly increases the risk of fatal injuries across all six states, with odds ratios ranging from 3.45 in California to 4.55 in Georgia. The findings provide data-driven insights that can support the development of targeted safety countermeasures and intelligent traffic management strategies to enhance safety at HRGCs.

Infrastructures doi: 10.3390/infrastructures11030088

Authors: Tong Xing Xufan Wang Like Pan Yang Song Dehai Zhang Qun Yu

With the rapid development of high-speed railways, the dynamic performance of the pantograph–catenary system plays a crucial role in ensuring the safe and stable operation of trains. This study investigates the effect of the structural parameters of the pantograph–catenary system to achieve good dynamic interaction performance under high-speed conditions. A finite element model of the catenary system, incorporating nonlinear cable and truss elements, and a lumped mass model of the pantograph are developed. The penalty function method is employed to simulate the pantograph–catenary interaction. A total of 2187 dynamic simulations are performed, with seven variables—pantograph parameters, span length, contact wire tension, messenger wire tension, number of droppers, stitch wire length, and stitch wire tension. The comprehensive effect of these parameters is evaluated based on dynamic performance indicators, such as pantograph–catenary contact force, pantograph head lift, and support point lift. The results indicate that increasing the number of droppers, contact wire tension, and messenger wire tension enhances dynamic performance, while an increase in span length negatively affects performance. Stitch wire tension has little to no effect.

Infrastructures doi: 10.3390/infrastructures11030087

Authors: Ugur Mutlu Sakdirat Kaewunruen

Railway infrastructure faces growing degradation risks from intensified operational loads and climate change, necessitating a paradigm shift from reactive repairs to digitalized predictive maintenance. This study explores the synergistic convergence of Artificial Intelligence (AI), Building Information Modeling (BIM), and Digital Twins (DT) to optimize asset management. A Systematic Literature Review was conducted, adhering to PRISMA guidelines and strictly selecting and analyzing 73 peer-reviewed articles from Web of Science and Scopus (2015–2026). The results reveal that while Supervised Learning remains the dominant paradigm for defect detection, Reinforcement Learning is emerging as a key tool for maintenance scheduling. However, a critical “Digital Twin Gap” is identified, where most systems function only as unidirectional digital representations rather than bidirectional, self-correcting twins. Furthermore, despite frequent sustainability claims, there is a marked absence of quantified environmental metrics in current research. Consequently, this paper concludes that future advancements must prioritize the development of “True Digital Twins” with autonomous actuation, ensure interoperability through Industry Foundation Classes (IFC), and integrate explicit “Green KPIs” to objectively validate the environmental benefits of digitalized maintenance strategies.

Infrastructures doi: 10.3390/infrastructures11030085

Authors: Pedro Hernández-Varona Ivan Félix-González Rolando Salgado-Estrada

The short service life of oil fields and limited oil deposits in shallow waters requires a constant search for new oil fields in deeper waters. Compliant towers are one of the most suitable structures for water depths between 300 m and 600 m, where fixed structures are economically unfeasible. The principal characteristics of compliant towers include a minimal number of cross sections in their main structural elements throughout their height, combined with significant flexibility and buoyancy. Due to their flexibility and buoyancy, gravitational loads at the deck do not significantly impact the foundation. Moreover, compliant towers do not need advanced building systems, installation processes or special maintenance. Additionally, the large height of compliant towers reduces their natural frequencies, which prevents them from being within the frequency range of environmental forces capable of producing structural resonance. For this reason, efforts are made to design compliant towers to be as flexible as possible. Hence, this research is focused on examining a methodology for the structural analysis of compliant towers at ultimate and serviceability limit states for a water depth of 550 m in the Mexican waters of the Gulf of Mexico.

Infrastructures doi: 10.3390/infrastructures11030086

Authors: Jiji Panicker Koshy Panicker Praveen Nagarajan Santosh Gopalakrishnan Thampi

Concrete arch dams, which account for about 4% of large dams worldwide, are distinguished by their efficient geometry, economy, effective load distribution, and high storage capacity. Under thermal loads, they are susceptible to unusual behavior in terms of deflection and stresses due to geometrical peculiarities, construction methodology, and restraints, which in turn may cause potential failure. This paper analyzes the behavior of a 50-year-old double-curvature, high, thin concrete arch dam under extreme thermal loading and fluctuating water levels, using 3D linear elastic FEM analyses and monitoring data. It rigorously evaluates structural response—deflections and stresses—at salient locations and interaction zones under large temperature fluctuations, a key yet underexplored risk for thin concrete arch dams in tropical and equatorial regions. Using real monitoring data, the research also examines the effectiveness of rehabilitation measures designed to mitigate thermal impacts. Results indicate that the dam deflection reverses at extreme temperature drops and rises when the reservoir is at higher or lower levels, respectively, which is not unusual for thin concrete double-curvature arch dams. Long-term exposure to high extreme temperatures with low reservoir water levels may become a concern, as it can cause higher tensile stresses at salient points and significant dam deflections towards upstream.

Infrastructures doi: 10.3390/infrastructures11030084

Authors: Mohamed E. Fathi Mohamed E. El-Zoughiby Mohamed Mortagi Osama Youssf Mohanad Abdulazeez Ahmed M. Tahwia

This research investigates the shear performance of sustainable self-compacting reinforced geopolymer concrete (GPC) beams incorporating granite waste powder (GWP) and ground granulated blast-furnace slag (GGBFS) as eco-friendly binding agents through experimental and numerical analyses. Five geopolymer reinforced concrete beam specimens (100 mm × 150 mm × 1500 mm) were tested under two-point loading conditions to evaluate the influence of longitudinal reinforcement ratio (0.85% to 2.0%) and shear span-to-effective depth ratio on the structural shear performance. The experimental investigation revealed that geopolymer reinforced concrete beams exhibit shear behavior characteristics similar to conventional Portland cement concrete beams, with the 2.0% reinforcement ratio achieving 18.3% higher shear strength compared to the 0.85% reinforcement ratio, while shear capacity increased proportionally with increasing shear span-to-depth ratio. Experimental data, including load–displacement response, shear strength measurements, strain distributions, failure modes, and crack patterns, were studied. Finite element nonlinear analysis was conducted by modifying the concrete modulus and stress–strain relationships to reflect the properties of geopolymer concrete using ABAQUS software integrated with the concrete damaged plasticity model. The results demonstrated that for the tested geopolymer reinforced concrete beams, first cracking load, steel yielding load, and ultimate load capacity increased systematically with increasing tension steel reinforcement ratio and proportionally with higher shear span-to-depth ratios.

Infrastructures doi: 10.3390/infrastructures11030083

Authors: Federico Del Duca Giulia Del Serrone Paola Di Mascio Federica Frammartino Eleonora Luciano Laura Moretti

Aviation is currently facing one of its greatest challenges: reconciling growing traffic demand with the need to drastically reduce climate-altering emissions. Hydrogen has emerged as one of the most promising alternatives to decarbonize air transport. However, it poses significant challenges related to cryogenic storage, safety, and the adaptation of the airport infrastructure. Aprons represent a critical issue, as the increased volume of fuel tanks and different refueling protocols directly impact airport operational capacity. This research fits within this framework by analyzing a Code 4E Italian airport over three time horizons: 2025, with an all-kerosene fleet; 2035, with a 25% penetration of hydrogen-powered class A and B aircraft; and 2045, with a further increase in the hydrogen share (75% class A and B and 15% class C). The study evaluates apron capacity using fast-time simulation and compares the outcomes with an analytical model. The results show good consistency between theoretical and simulated capacity. The 2035 and 2045 scenarios with the introduction of hydrogen-powered aircraft show a reduction in apron capacity between 16% and 5% compared to conventional scenarios.

Infrastructures doi: 10.3390/infrastructures11030082

Authors: Leonel García José Manuel Gutiérrez-Moreno Alejandro Sánchez-Atondo Alejandro Mungaray-Moctezuma Marco Montoya-Alcaraz Julio Calderón-Ramírez

The objective of this research is to determine whether levels of road accessibility in urban, peri-urban, and sub-urban localities within the municipalities of Mexicali and San Felipe, in Baja California, Mexico, can be associated with processes of territorial expansion, population growth, and changes in urban marginalization levels. This is assessed through a methodology that combines ex-ante and ex-post analysis, the use of the Urban Marginalization Index (UMI) at the AGEB scale, and a hierarchical accessibility classification (Levels A, B, and C), thereby contributing a replicable tool for analyzing socio-spatial impacts derived from road infrastructure. To this end, modernization, maintenance, and reconstruction works, as well as the construction of an interchange carried out between 2006 and 2017 along Federal Highway No. 5—specifically the Mexicali–San Felipe section—were examined in relation to the accessibility they provide to ten nearby localities. UMI values were estimated for 134 AGEB using data from 2000, 2010, and 2020, which enabled the assessment of changes in quality of life before, during, and after the execution of these works. The results show significant population growth in six localities, accompanied by territorial expansion processes. Localities with direct connection to the study corridor tended to exhibit middle to low marginalization levels, while those with indirect accessibility or direct access through another federal highway section tended toward middle to high levels, with some shifting to middle to low. It is concluded that road accessibility constitutes a relevant factor in the progressive improvement in socioeconomic conditions and quality of life in urban, peri-urban, and sub-urban areas.

Infrastructures doi: 10.3390/infrastructures11030080

Authors: Gilmer Challco Dennis Apaza Daniel Rodriguez Erika Rodriguez Blanca Bautista Daniel Quiun

While steel sheets are an effective strengthening technique for existing structures, experimental evidence on galvanised steel sheets is limited, necessitating their evaluation as a durable and cost-effective solution for the flexural strengthening of reinforced concrete (RC) beams. This study analyses the influence of external reinforcement using galvanised steel sheets applied to RC beams. The structural behaviour of the specimens was assessed through flexural tests, with monotonic loading applied at one-third and two-thirds of the effective span, in accordance with ASTM C78 guidelines. In addition, an analytical model was formulated to capture the non-linear behaviour of concrete, reinforcing steel, and galvanised steel sheets. The results indicate that beams strengthened with external reinforcement exhibit an increase in load-bearing capacity of up to 69% in the elastic range, together with significant improvements in ductility of up to 22%. Moreover, the use of vertical U-wrap sheets and anchor bolts enhances the bond between the sheets and the concrete, thereby reducing the risk of premature debonding. Overall, the findings confirm that the use of galvanised steel sheets is an effective and practical strengthening technique for improving the flexural performance of RC beams.

Infrastructures doi: 10.3390/infrastructures11030081

Authors: Paulo Ricardo Lemos de Santana Elane Donato Santos Fernando Santos do Amor Divino Luana Pereira de Jesus Weiner Gustavo Silva Costa Acbal Rucas Andrade Achy Mario Sergio de Souza Almeida

The reuse of milled pavement material, known as RAP (Reclaimed Asphalt Pavement), represents one of the major current challenges in highway engineering worldwide. There is no doubt that the most valuable application of this residue is its use in the production of new hot asphalt mixtures, incorporating the highest possible RAP content, a process that requires adaptations in residue processing at asphalt plants. In Brazil, the RAP content added to these mixtures is limited to a maximum of 25%. Consequently, alternative applications have gained prominence in the country to increase RAP utilization in pavement engineering, such as its use in cold premixed asphalt mixtures. This study aimed to evaluate the performance of cold asphalt mixtures containing different RAP contents through mechanistic-empirical analyses of a reference pavement structure, using the modelling framework adopted in the Brazilian Asphalt Pavement Design Method (MeDiNa). After Marshall mix design and volumetric and mechanical characterization of mixtures containing 0%, 10%, 20%, 30%, and 40% RAP, stiffness and fatigue parameters were used to estimate the evolution of cracked area in the reference pavement, with each mixture applied as the surface layer under different traffic levels. The results demonstrated that pavement performance improved for all RAP contents evaluated compared to the mixture without RAP, with the mixture containing 30% RAP showing the best overall performance.

Infrastructures doi: 10.3390/infrastructures11030079

Authors: Giulia Delo Camilla Corbani Cecilia Surace

Underground structures are becoming increasingly vital components of modern transportation networks and urban systems, making their structural integrity a critical factor for safety and operational reliability. However, despite considerable progress in Structural Health Monitoring (SHM), the application of data-driven and vibration-based strategies to underground infrastructures remains an open and under-explored field, often because of limited data availability. Population-Based Structural Health Monitoring (PBSHM) offers a promising pathway to overcome this challenge by leveraging transfer learning to share diagnostic knowledge among similar structures. This study investigates the feasibility of extending the PBSHM paradigm to underground infrastructures, with a particular focus on a metro tunnel application. Through dynamic finite element simulations, relevant vibration features are identified, and damage detection strategies based on transmissibilities and cross-correlation functions are evaluated. The numerical results show that transmissibility-based indicators enable accurate damage localisation along the tunnel lining, even under noisy conditions. In contrast, cross-correlation features exhibit more limited performance in some configurations. Building on this evidence, the transmissibility-based damage indicator is subsequently embedded within the PBSHM framework and used as a transferable feature between tunnel models, achieving reliable damage detection in a second tunnel with heterogeneous characteristics, with F1 scores exceeding 80% for all considered damage severities and above 94% for the most critical case, thereby highlighting the potential of knowledge transfer for large-scale underground networks.

Infrastructures doi: 10.3390/infrastructures11030078

Authors: Šárka Pešková Vít Šmilauer Pavel Horák Rostislav Šulc Martin Válek Petr Vítek Pavel Růžička

Segmental tunnel linings represent a conventional method commonly employed in tunnel boring machine (TBM) operations. However, this approach presents notable limitations, including handling challenges and the presence of numerous joints prone to leakage. An alternative method involving cast-in-place tunnel lining was experimentally investigated through a scaled mock-up test conducted at approximately 1:4 scale, with a total length of 0.85 m and 2 m lining diameter. In this setup, two reinforced concrete rings were constructed to simulate the surrounding geological conditions and internal formwork. Fiber-reinforced concrete was then pumped into the annular space between the rings, forming a cast-in-place lining with a thickness of 170 mm. To replicate the thrust force exerted by hydraulic actuators of a TBM, a hydrostatic pressure up to 5 MPa was applied from the front side. The experiment demonstrated a linear compaction of fresh concrete by approximately 3%, greater resistance to compaction in the lower section, and a uniformly well-compacted concrete structure throughout the entire volume.

Infrastructures doi: 10.3390/infrastructures11030077

Authors: Ahmed Elsayed Ahmed Abdel-Rahim Logan Prescott

Pedestrian and cyclist safety at urban intersections remains a critical challenge for transportation agencies, as vulnerable road users are significantly exposed to crash risks in complex traffic environments. Identifying high-risk locations and factors that contribute to crashes is essential for improving road safety. This study developed an explainable machine learning framework to predict motor vehicle-involved pedestrian and cyclist crash occurrence at urban intersections using five years of crash, geometric, operational, and socioeconomic data from a large set of urban intersections. Five supervised machine learning algorithms were trained and evaluated, including Binary Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Decision Tree, and Random Forest. The evaluated models demonstrated strong predictive performance overall, with accuracies approaching 91% and high discriminative capability. In particular, the Binary Logistic Regression and Random Forest models achieved the highest area under the receiver operating characteristic curve (AUC) values of 0.961 and 0.964, respectively. To enhance transparency, SHAP values were used to quantify the contribution of predictors and examine feature effects at both the global and local levels. The results indicate that roadway hierarchy, intersection markings, and total entering volume are among the most influential determinants of crash likelihood, while socioeconomic variables exhibit weaker but interpretable effects.

Infrastructures doi: 10.3390/infrastructures11030076

Authors: Ahmed S. Eisa Hilal Hassan Mohamed A. Badran Ayman El-Zohairy

Bridges are critical components of transportation networks, and fire accidents can significantly impair their structural integrity, leading to safety risks and major economic losses. This study presents a comprehensive inspection, materials testing, repair, and field load testing program for a full-scale concrete box girder bridge (Delta Bridge, Alexandria, Egypt) following a fire exposure on two spans. A total of 28 concrete core samples were extracted and tested, revealing average compressive strengths of 48.50 MPa (slab), 53.90 MPa (web), and 45.88 MPa (columns), representing moderate reductions of approximately 8.5%, 7.9%, and 10.8%, respectively, relative to the original in situ concrete strength recorded during construction, and 29.2%, 43.7%, and 30.0% increases over the minimum acceptance limits specified by Egyptian code of practice (ECP 203). Tensile strength tests on reinforcement bars indicated an average yield strength reduction coefficient of 0.87, corresponding to an estimated peak exposure temperature of 600 °C, yet still satisfying Egyptian code requirements (≥500 MPa). Field static load tests using 40-ton tri-axle trucks demonstrated maximum midspan deflections of 6.7 mm in fire-exposed spans and full recovery (>94%) upon unloading, confirming that the residual stiffness and load-carrying capacity were within acceptable limits. Based on these results, a targeted repair program was executed, including concrete cover replacement with shotcrete; steel derusting; surface coating; and bearing replacement, followed by a verification load test that confirmed the effectiveness of the rehabilitation. This case study demonstrates a robust framework for post-fire condition assessment, residual capacity evaluation, and repair validation of concrete box girder bridges. The methodology and findings provide valuable guidance for engineers and transportation authorities in mitigating fire-induced risks and ensuring the safe reopening of critical bridge infrastructure.

Infrastructures doi: 10.3390/infrastructures11030075

Authors: Gema García Travé Raúl Tauste Martínez Fernando Moreno Navarro María del Carmen Rubio Gámez

The main goal of this study is to evaluate the feasibility of designing high-performance MASAI mixtures produced at ambient temperature. For this purpose, the impacts of certain variables, such as the type and amount of asphalt emulsion and the use or non-use of RAP, on its performance are evaluated. Subsequently, its stiffness modulus, tensile strength, permanent deformation, and resistance to thermal cracking were evaluated and compared against a conventional dense-graded asphalt concrete (AC 16) and an open-graded (BBTM11B) hot-mix asphalt used for wearing courses. The results showed that these materials could represent more sustainable and good solutions for the rehabilitation of some types of pavements.

Infrastructures doi: 10.3390/infrastructures11030074

Authors: Talal Athobaiti Ahmed M. Tahwia Rajab Abousnina Mohamed Mortagi Osama Youssf

The rising environmental burden of Portland cement production has intensified the demand for eco-friendly binders that support sustainable construction. This study investigates the development and performance of eco-friendly self-compacting geopolymer concrete (SCGC) produced from industrial by-products, including fly ash (FA), ground granulated blast furnace slag (GGBFS), silica fume (SF), metakaolin (MK), and glass waste powder (GWP). Twenty-one binder formulations were evaluated for fresh-state workability, mechanical performance, durability, and microstructural characteristics under different curing regimes. Fresh properties were assessed using slump flow, V-funnel, L-box, and J-ring tests, while hardened-state evaluations included compressive and flexural strength, Young’s modulus, and water absorption. Scanning electron microscopy (SEM) and X-ray diffraction (XRD) analysis were performed on selected mixes to examine microstructural features and crystalline phase development. Results highlight a strong dependency of SCGC performance on binder composition and curing conditions. Mixes rich in GGBFS and SF demonstrated superior mechanical and durability performance, achieving compressive strengths of up to 102.4 MPa under water curing and 107.6 MPa under heat curing, along with negligible water absorption, reflecting a dense and well-developed gel matrix. SEM micrographs confirmed homogeneous, compact microstructures in high-performing mixes, while XRD analysis revealed broad amorphous humps indicative of well-formed N-A-S-H and C-A-S-H gel phases with minimal crystalline residues. In contrast, FA-dominant mixes displayed delayed strength development, and MK-GWP-rich systems exhibited higher porosity and reduced strength. This study underscores the significance of precursor synergy, optimized curing strategies, and microstructural refinement in tailoring SCGC for high-performance, durable, and low-carbon applications in sustainable construction with values ranged from 38.64 GPa (Mix 21) to 25.04 GPa (Mix 19) at 28 days. Stiffer mixes corresponded to denser matrices containing GGBFS and silica fume, whereas lower values were linked to weaker bonding and higher porosity.

Infrastructures doi: 10.3390/infrastructures11030073

Authors: Rajab Abousnina Fahad Aljuaydi

The construction industry is under increasing pressure to reduce cement consumption and associated CO2 emissions while managing the growing generation of industrial by-products. Granite stone powder (GSP), produced in large quantities during aggregate crushing operations, is commonly treated as waste despite its potential application in cementitious systems. This study evaluates the feasibility of using GSP as a supplementary cementitious material (SCM) in cement mortars, benchmarked against fly ash (FA). Cement mortars were prepared with 0%, 10%, 15%, 20%, and 25% replacement of Ordinary Portland Cement (OPC) using GSP and, for comparison, identical replacement levels of FA. Fresh behaviour, physical properties, mechanical performance, and microstructural characteristics were evaluated using flow tests, isothermal calorimetry, SEM, and XRF. FA and GSP exhibited distinct effects on mortar performance. FA improved workability at higher replacement levels, with flow diameter increasing by 2% above the control at 25% replacement, whereas GSP progressively reduced flowability by approximately 33% at 25% replacement due to its angular particle shape and higher surface area. Hydration analysis showed that both SCMs reduced early hydration intensity compared to the control; however, GSP consistently exhibited higher peak heat-flow values than FA, indicating stronger early-age hydration supported by physical filler and nucleation effects. At 28 days, the 10% GSP mixture achieved 30 MPa, retaining about 94% of the control strength (32 MPa), while FA mixtures showed strength reductions exceeding 23% at comparable replacement levels. Granite stone powder is most effective at low replacement levels (10%), where it promotes early hydration, improves matrix densification, and preserves compressive strength, demonstrating its suitability as a low-carbon supplementary cementitious material in cement-based construction.

Infrastructures doi: 10.3390/infrastructures11020072

Authors: Nitidetch Koohathongsumrit Wasana Chankham

Underground tunnel construction projects using tunnel boring machines (TBMs) require a holistic risk perspective. Such projects face various risks arising from social, economic, political, workforce, and regulatory aspects during project execution. It is necessary to develop preventive strategies for managing these risks and thereby ensure timely project delivery, cost efficiency, and safety. In this study, we aimed to develop a comprehensive hybrid decision-making framework for analyzing risks in TBM-based tunnel construction projects. The proposed approach integrates the best–worst method (BWM), data envelopment analysis (DEA) model-based risk assessment, and the preference ranking organization method for enrichment evaluation (PROMETHEE). The BWM was applied to determine the weights of decision criteria with fewer comparisons and improved consistency. Subsequently, the DEA model was then used to compute local risk scores under multiple input and output conditions. Finally, PROMETHEE was employed to analyze the risks based on positive and negative outranking flows. The proposed approach was applied to a realistic metro construction project in Bangkok. The findings indicated that the proposed approach effectively compromised all the decision-making attributes to manage the uncertainties. The proposed methodology can support project managers, stakeholders, engineers, and relevant authorities in identifying high-priority risks and implementing effective mitigation strategies to enhance risk management in tunnel construction.

Infrastructures doi: 10.3390/infrastructures11020071

Authors: Théotime Michez Laurent Peyras Stéphane Lambert Sébastien Reynaud Patrick Garcin

In mountain areas, long linear transport infrastructures (roads, motorways, railways, etc.) are exposed to numerous natural hazards, especially hydrological and gravity-driven events such as slope instabilities, rockfalls, or torrential hazards. These phenomena can damage infrastructure, or even lead to the destruction of large sections, causing a risk for users and a deterioration of service. Infrastructure managers face several difficulties in handling these risks. One of them is identifying and representing them, due to the scale of the infrastructure, which is composed of numerous structures and exposed to multiple hazards. In this context, a model is proposed to represent all potential failure scenarios for such infrastructures. This model is based on system reliability analysis methods: functional analysis, failure mode and effect analysis (FMEA), and fault tree analysis (FTA). It is intended to be applied to a linear infrastructure, several kilometres long, exposed to various hazards. The proposed approach allows for the identification of all possible failure modes, including damage to structures and its functional consequences. Its applicability is being tested on a simple case study.

Infrastructures doi: 10.3390/infrastructures11020070

Authors: Pablo Julián López-González Oscar Moreno-Vázquez Sergio Aurelio Zamora-Castro Tania Irene Lagunes-Vega Efrén Meza-Ruíz Brenda Suemy Trujillo-García Rodrigo Vivar-Ocampo David Reyes-González Joaquín Sangabriel-Lomelí

This study investigates alkali-activated recycled pumice as a sustainable cement replacement for hydraulic concrete used in rigid pavements. Cement was replaced at 15%, 25%, and 50% by mass and activated using NaOH solutions at 1 N, 0.5 N, and 0.25 N, resulting in nine mixture variants. Mechanical performance was assessed through compressive strength at 7, 14, and 28 days, and flexural strength at 28 days. Durability was evaluated via natural carbonation depth at 210 and 1090 days. X-ray diffraction (XRD) identified aluminosilicate phases in the pumice, supporting its alkali-reactive potential. Mixtures with 15% pumice replacement achieved compressive strengths up to 20.99 MPa, comparable to the control mix (20.45 MPa), whereas 25% and 50% replacements produced moderate strength reductions. Flexural strength in 15% mixtures (7.38–7.44 MPa) was also comparable to the control (7.30 MPa), while higher replacement levels reduced flexural performance. Carbonation resistance improved for mixtures with an optimized alkaline-to-pumice ratio (APR, defined as NaOH concentration relative to pumice content) between 0.0167 and 0.02, indicating more balanced activation and reduced CO2 ingress. Overall, alkali-activated recycled pumice enables partial cement replacement while maintaining mechanical performance and carbonation resistance at 15% substitution, supporting circular economy strategies and lowering the carbon footprint of rigid pavement concrete.