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Infrastructures
Volume 11
Issue 7
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Infrastructures, Volume 11, Issue 7 (July 2026) – 46 articles

Cover Story (view full-size image): Many existing spillway piers in Japan were constructed more than 60 years ago, using round rebars and very low rebar ratios. Their seismic response may be influenced by post-cracking bond–slip. This study applies Incremental Dynamic Analysis to an actual spillway pier, using a beam model and a 3D finite element analysis that accounts for bond–slip. Parametric analyses examine the effects of bar diameter, rebar ratio, bond condition, and earthquake intensity. The beam model is useful for global screening but may misclassify the damage mode, whereas the 3D model captures flexure-dominant behavior and quantifies displacement, rebar strain, and tensile and compression damages. The findings support a staged strategy combining beam models for global screening with 3D analysis for detailed assessment. View this paper
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16 pages, 12565 KB  
Article
Time-Varying Temperatures of Early Age Massive Concrete in #0 Segment of Huangsha Harbor Bridge
by Xiao-Xiang Cheng, Ze-Yang Sun and Hong Zhu
Infrastructures 2026, 11(7), 255; https://doi.org/10.3390/infrastructures11070255 - 22 Jul 2026
Viewed by 286
Abstract
To accurately predict temperature rise due to the concrete hydration heat released from the #0 segment of a continuous concrete girder bridge at an early construction stage for structural design purposes, researchers proposed an approach incorporating empirical predictive formulae with a preliminary numerical [...] Read more.
To accurately predict temperature rise due to the concrete hydration heat released from the #0 segment of a continuous concrete girder bridge at an early construction stage for structural design purposes, researchers proposed an approach incorporating empirical predictive formulae with a preliminary numerical analysis. However, due to the uniqueness of the structural geometry and material in each engineering case and the limited data shared by the whole engineering community, no universal predictive empirical model for temperature rise due to hydration heat has yet been identified for practical use that can be applied to a variety of different projects. Moreover, the preliminary numerical analyses are usually based on questionable assumptions and simplifications of the physical truth, the accuracy of which also requires further validation. To this end, the present research measured the time-varying temperature samples of early age massive concrete in the #0 segment of Huangsha Harbor Bridge (a twin-deck three-span continuous concrete box girder bridge located in Jiangsu Province, China) and examined the accuracy of the predictive empirical models formulated by other researchers and the usability of a numerical modal established on a commercial finite element (FE) platform by comparing the corresponding results with the data from the present field measurements. The results suggest that the empirical formulae proposed can generally effectively describe the actual temperature distribution patterns related to the thermal issue, but they are characterized by inferior usability in some cases. In addition, the present comparison also indicates that the actual maximum temperature rise can be correctly predicted by the preliminary FE analysis in most cases. Full article
(This article belongs to the Section Infrastructures and Structural Engineering)
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38 pages, 3279 KB  
Article
A Physics-Constrained Multi-Task Learning–Semi-Markov Framework for Bridge Condition Assessment, Deterioration Forecasting, and Risk-Aware Maintenance Prioritization
by Zhihui Feng, Yuchen Zhao, Liangqi Zhang, Fulei Xu, Xiaojun Li, Zhiqiang Liang, Yufeng Guo and Hui Zhang
Infrastructures 2026, 11(7), 254; https://doi.org/10.3390/infrastructures11070254 - 22 Jul 2026
Viewed by 363
Abstract
Bridge health assessment and deterioration prediction are essential for traffic safety and maintenance planning. However, conventional bridge evaluation still relies heavily on expert judgment and heuristic rules, limiting objectivity and long-term forecasting capability. This study proposes a physics-constrained multi-task learning–semi-Markov framework for bridge [...] Read more.
Bridge health assessment and deterioration prediction are essential for traffic safety and maintenance planning. However, conventional bridge evaluation still relies heavily on expert judgment and heuristic rules, limiting objectivity and long-term forecasting capability. This study proposes a physics-constrained multi-task learning–semi-Markov framework for bridge condition assessment, multi-year deterioration forecasting, and risk-aware maintenance prioritization. Guided by the Highway Bridge Technical Condition Rating Code, the proposed model jointly predicts health grade, defect type, and severity from inspection item/defect entry records, improving assessment robustness through cross-task information sharing. The predicted health states are then incorporated into a physics-constrained semi-Markov model to forecast bridge deterioration over a three-year horizon, and the current and predicted states are further used for maintenance prioritization. Experiments on real bridge inspection data show that the proposed model achieves test accuracies of 95.20%, 99.67%, and 99.72% for health grade, defect type, and severity, respectively, with a Cohen’s kappa of 0.872, while the proposed semi-Markov model achieves a three-year prediction accuracy of 95.4% and a weighted accuracy of 99.1%. Comparative and ablation studies further demonstrate the effectiveness of the proposed framework for bridge lifecycle management. Full article
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32 pages, 18679 KB  
Article
Hydraulic and Scour Assessment for Sustainable Bridge Replacement over the Mid Fork Saline River, USA
by Ahmad J. Alzubaidi, Haneen H. Darwish, Mutaz M. Zoubi, Qusay Y. Abu-Afifeh, Rasha Al-Rkebat, Heba F. Al-Jawaldeh, Nisreen Obeidat, Tariq M. F. Al-Nawaiseh, Ali Brezat, Saif Al-Omari and Yazan A. Alta’any
Infrastructures 2026, 11(7), 253; https://doi.org/10.3390/infrastructures11070253 - 22 Jul 2026
Viewed by 499
Abstract
River crossing bridges in low-gradient floodplains can be affected by limited conveyance, backwater control, and scour-related foundation risk. This study evaluates a proposed IL 13 bridge replacement over the Mid Fork Saline River, Illinois, using HEC-RAS 1D steady-flow modeling, hydrologic inputs from USGS [...] Read more.
River crossing bridges in low-gradient floodplains can be affected by limited conveyance, backwater control, and scour-related foundation risk. This study evaluates a proposed IL 13 bridge replacement over the Mid Fork Saline River, Illinois, using HEC-RAS 1D steady-flow modeling, hydrologic inputs from USGS StreamStats for a drainage area of 236.45 mi2, bridge opening analysis, multiple-opening interpretation, and HEC-18 scour assessment. Natural, existing, and proposed conditions were compared under design floods and Ohio River tailwater scenarios. The proposed bridge increased the effective waterway opening under all evaluated hydraulic scenarios, with increases of approximately 68.5–79.5% under the no-tailwater case, 76.1–76.9% under the 10-year Ohio River tailwater case, and 71.7–72.1% under the 50-year Ohio River tailwater case. Bridge opening velocity decreased by about one-third, indicating lower local hydraulic intensity and improved conveyance through the main opening. Contraction scour was not controlling, while computed pier scour decreased by approximately 8–10% and the controlling right abutment scour decreased slightly. Because empirical HEC-18 scour equations can have large uncertainty, commonly approaching an order of a factor of two in practical scour prediction, these reductions are interpreted only as comparative trends. They do not provide a basis for reducing foundation design requirements, but they indicate that the proposed replacement does not worsen the controlling scour response. Overall, the replacement improves hydraulic compatibility, reduces local hydraulic stress, and does not worsen the governing scour response. The study supports SDG 9, SDG 11, and SDG 13 in a hydraulic-infrastructure sense by promoting resilient bridge serviceability, safer transport connectivity, and adaptation-oriented flood risk assessment; however, full life-cycle carbon, cost, and network-resilience metrics were outside the scope. Full article
(This article belongs to the Special Issue Sustainable Bridge Engineering)
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24 pages, 487 KB  
Article
Sustainable Pavement Maintenance and Rehabilitation Planning Using a Big-Data Based Microscopic Management Model
by Hamed Maleki, Mohammad Bagher Fakhrzad, Fereidoon Moghadas Nejad, Hamzeh Zakeri and Akbar Danesh
Infrastructures 2026, 11(7), 252; https://doi.org/10.3390/infrastructures11070252 - 21 Jul 2026
Viewed by 416
Abstract
The condition of pavement networks gradually deteriorates over years of use. Finding a suitable strategy to address this deterioration has become a key concern in pavement maintenance. Recently, pavement agencies have been facing uncertainties in maintenance and rehabilitation activities because of economic conditions [...] Read more.
The condition of pavement networks gradually deteriorates over years of use. Finding a suitable strategy to address this deterioration has become a key concern in pavement maintenance. Recently, pavement agencies have been facing uncertainties in maintenance and rehabilitation activities because of economic conditions and changes in climatic and traffic conditions, which complicate planning for strategy determination. Therefore, it is important for pavement agencies to be able to maximize pavement condition while considering uncertainty and minimizing the maintenance budget. In this paper, a pavement management model has been developed using a microscopic approach to overcome the complexity. The microscopic pavement management problem is formulated as an integer linear programming model, subject to budget constraints. The proposed microscopic model incorporates integer variables representing the pavement sections to be treated by the applicable maintenance and rehabilitation actions. Innovative approaches, cold paving techniques, are applied in the paper, offering substantial benefits in terms of environmental impact and resource efficiency. In the proposed model, distribution functions fitted to historical data are used to evaluate pavement condition performance. The objective of yielding optimum pavement conditions is achieved by considering uncertainty applied to a given pavement system. A case study was conducted by examining a network of eight pavement sections over a 5-year planning period. The model solutions are obtained by integrating activities and the epsilon-constraint method. In addition, the results of each solution are compared for the decision-maker. The results show that the proposed model is an attractive method for managing pavement maintenance programs at the network level. Full article
(This article belongs to the Special Issue Cold and Warm Techniques for Sustainable Pavement Construction and Maintenance)
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17 pages, 860 KB  
Article
Mechanical and Volumetric Properties of Hot Mix Asphalt with Rice and Wheat Husk Waste as Alternative Filler
by Abdul Hafeez Memon, Naeem Aziz Memon, Giuseppe Loprencipe, Antonio D’Andrea, Gulzar Hussain Jatoi and Laura Moretti
Infrastructures 2026, 11(7), 251; https://doi.org/10.3390/infrastructures11070251 - 21 Jul 2026
Viewed by 305
Abstract
Fillers (<0.075 mm) in hot mix asphalt (HMA) play a pivotal role in optimizing bitumen content, filling voids, and improving mechanical performance. In many agricultural countries, large quantities of rice and wheat husk waste are produced, while the road construction industry faces material [...] Read more.
Fillers (<0.075 mm) in hot mix asphalt (HMA) play a pivotal role in optimizing bitumen content, filling voids, and improving mechanical performance. In many agricultural countries, large quantities of rice and wheat husk waste are produced, while the road construction industry faces material shortages of conventional filler materials and related performance challenges. This study evaluates the feasibility of using rice husk (RH) and wheat husk (WH) fillers on HMA performance. Unlike previous studies that primarily focused on ash-derived agricultural residues, this work investigates the direct utilization of raw husk materials, eliminating the need for energy-intensive processing. Few studies directly examine the aggregate gradation and binder concentration with respect to rice and wheat husk ash. As a result, the relative effectiveness of these two agricultural waste fillers in improving the volumetric and Marshall properties of asphalt mixtures is yet unknown. Fifteen mixtures with varying bitumen contents (3.0–5.0%) were tested to determine the optimum bitumen content (OBC). Subsequently, modified mixtures were prepared at the OBC using RH and WH fillers at five replacement levels (5.0–15.0%). The Marshall Mix design method was employed to assess stability, flow, density, and air voids content. The control mixture showed a Marshall stability of 14.86 kN, flow of 3.52 mm, density of 2.342 g/cm3, and air voids of 2.9%. At their optimum filler contents (i.e., 10.33% for RH and 10.43% for WH), the modified mixtures achieved higher Marshall stability (14.96 kN and 15.06 kN, respectively), with flow values of 3.51 mm and 2.83 mm, and densities of 2.335 g/cm3 and 2.330 g/cm3. Statistical analysis using ANOVA at the OBC confirmed that RH and WH fillers can be used as alternative fillers in HMA without adversely affecting Marshall performance, while contributing to agricultural waste valorization and resource conservation. Full article
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21 pages, 33652 KB  
Article
Evaluation of the Performance Capability of Remote Visual Inspection of Concrete Structures Using Drones
by George T. Alliott, Adam C. Bannister and Hamish Dow
Infrastructures 2026, 11(7), 250; https://doi.org/10.3390/infrastructures11070250 - 21 Jul 2026
Viewed by 336
Abstract
Close visual inspection (CVI) forms a cornerstone of asset integrity. Advances in access technologies, including drones, have led to their increased use for remote visual inspection (RVI). However, comparative studies of RVI and CVI, in terms of defect detection, are currently limited. In [...] Read more.
Close visual inspection (CVI) forms a cornerstone of asset integrity. Advances in access technologies, including drones, have led to their increased use for remote visual inspection (RVI). However, comparative studies of RVI and CVI, in terms of defect detection, are currently limited. In this study, controlled trials were conducted with multiple industrial participants operating drones to inspect a concrete block wall containing representative defects. RVI performance was assessed in terms of defect detection, identification and sizing. RVI demonstrated moderate performance, with an overall defect detection rate of approximately 50% and no participant exceeding 0.6. Detection was strongly dependent on defect type, with larger defects such as spalling and chipping consistently identified, while finer defects such as cracking were frequently missed. Identification of defect type was less reliable and influenced by inspector experience, while sizing capability was limited, with only one participant providing approximate measurements for larger defects. An automated visual inspection device, termed ALICS (Adaptive Lighting for the Inspection of Concrete Structures), was deployed on two samples. Images were captured of the concrete surface under varying lighting conditions to enhance the visibility of any present defects. Images were then analysed using artificial intelligence (AI), with the device identifying all defects in the tested areas. These results highlight both the current limitations of RVI and the potential of illumination-enhanced automated approaches to improve inspection reliability. Full article
(This article belongs to the Section Infrastructures Inspection and Maintenance)
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28 pages, 10880 KB  
Article
On the Cost Analysis of Low-Noise Pavements
by Filippo Giammaria Praticò and Ezgi Eren
Infrastructures 2026, 11(7), 249; https://doi.org/10.3390/infrastructures11070249 - 21 Jul 2026
Viewed by 316
Abstract
Low-noise pavements (LNPs) are increasingly important under Green Public Procurement policies, yet public administrations still lack clear guidance on selecting pavement types based on noise-related externalities. Although traffic noise generates substantial societal costs—affecting health, education, and property values—these external burdens are often overlooked [...] Read more.
Low-noise pavements (LNPs) are increasingly important under Green Public Procurement policies, yet public administrations still lack clear guidance on selecting pavement types based on noise-related externalities. Although traffic noise generates substantial societal costs—affecting health, education, and property values—these external burdens are often overlooked or excluded from traditional pavement appraisal and investment decisions, leading to systematically underestimated life cycle costs (LCC). This study develops an integrated framework to monetise traffic-noise impacts within an LCC perspective by combining health effects (Disability-Adjusted Life Years, DALYs), property-value capitalisation (willingness to pay, WTP), and noise-induced educational losses. The system limit is intentionally restricted to noise-related externalities during pavement operations, while agency, user, and vehicle operating costs are excluded. A comprehensive review of existing monetisation approaches is provided, and a new unified method is proposed. The framework is applied to a case study from the LIFE SNEAK project on Via La Marmora (Florence, Italy), comparing existing, acoustically non-optimised, and acoustically optimised surfaces. The results showed that the LIFE SNEAK pavement significantly alleviated the burden of noise on public health and education costs, which were 34% and 33% lower than in the baseline scenario, respectively, with a welfare surplus of +€2.38 million over the ten-year period. In particular, it was noted that the most important economic contribution of LNPs stems from the WTP approach. This study provides clear evidence that noise externalities play a considerable role in long-term pavement cost estimates, thereby supporting the systematic inclusion of these costs in LCC analyses. The proposed method puts forward a practical approach to support the selection of noise-sensitive, sustainable, and socially responsible road pavements. Full article
(This article belongs to the Special Issue Sustainable Materials and Design for Wearing Courses and Surface Treatments)
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29 pages, 12276 KB  
Article
Performance Evaluation of a Tunnel–Slope System
by Juan M. Mayoral, Paola Martínez, Mauricio Pérez, A. Román-de la Sancha and Jose Francisco Suárez-Fino
Infrastructures 2026, 11(7), 248; https://doi.org/10.3390/infrastructures11070248 - 20 Jul 2026
Viewed by 313
Abstract
Intense rainfall and the resulting increase in ground saturation can significantly modify the mechanical performance of rock masses in natural slopes, particularly when fractured material is present. Extended infiltration reduces shear strength along discontinuities and increases pore-water pressures, raising the probability of large-scale [...] Read more.
Intense rainfall and the resulting increase in ground saturation can significantly modify the mechanical performance of rock masses in natural slopes, particularly when fractured material is present. Extended infiltration reduces shear strength along discontinuities and increases pore-water pressures, raising the probability of large-scale landslides. When a tunnel is built within or near an unstable slope, the response of both structures becomes coupled, and this tunnel–slope interaction has proven to be an important aspect in the design and safety assessment of underground infrastructure in mountainous regions. This study evaluates the static and seismic performance of a tunnel–slope system in a fractured shale–limestone slope that failed after heavy rainfall. Since ground exploration was limited, the observed failure was reproduced through a back-analysis within a performance-based design (PBD) framework to calibrate representative geomechanical parameters. These parameters were then used in three-dimensional finite difference models to simulate the tunnel construction process and the seismic response of the system. During construction, the interaction between the tunnel and the slope was found to be minor. Under seismic loading, however, the simulations revealed notable interaction effects: slope displacements accumulate in the zone where the tunnel runs closest to the unstable critical section, and the stresses in the tunnel lining increase as a result of both the interaction with the slope and the curvature of the alignment. These results indicate that tunnel–slope interaction should be explicitly considered in the analysis and design of underground infrastructure whenever the tunnel lies within about four diameters of an unstable slope. Full article
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33 pages, 29256 KB  
Article
Constrained LLM Reporting for Geospatial Climate Risk: A One-Shot In-Context Framework for Critical Infrastructure
by Farid Arabameri, Jörn Plönnigs, Maryam Imani and Panagiotis Spyridis
Infrastructures 2026, 11(7), 247; https://doi.org/10.3390/infrastructures11070247 - 20 Jul 2026
Viewed by 305
Abstract
Climate risk assessments for critical infrastructure are essential to identifying and predicting vulnerabilities early in the asset life cycle, enabling proactive mitigation through the implementation of technical and nature-based solutions (NbS) before impacts occur. However, such assessments often rely on dense quantitative indices [...] Read more.
Climate risk assessments for critical infrastructure are essential to identifying and predicting vulnerabilities early in the asset life cycle, enabling proactive mitigation through the implementation of technical and nature-based solutions (NbS) before impacts occur. However, such assessments often rely on dense quantitative indices that are difficult for non-technical stakeholders to interpret. To address this challenge, this paper presents an open-source decision support platform that combines OpenStreetMap site characterization, qualitative pre-screening, a quantitative IPCC AR6-aligned risk chain, and a downstream NbS recommendation layer. The approach deploys Large Language Models (LLMs) to translate analytical outputs into accessible narrative explanations. End-to-end site-characterization processing across three European demonstration sites took between 29 and 70 s. An exploratory ablation study investigated the faithfulness of the AI-generated explanations using three complementary metrics, demonstrating that the generated hazard assessments remained factually grounded and free from fabricated numerical values. Introducing example reports (exemplars) into the prompt context further stabilized the reliability of the output for complex risk indicators. Finally, a small blind expert evaluation with six researchers from adjacent technical domains provided convergent evidence: five of six raters independently rated with-exemplar Hazard Reports higher on completeness; among the five raters who expressed a directional preference, all five favored the with-exemplar condition (sign test, p = 0.031). Furthermore, seven of eight aggregate dimension-level comparisons confirmed that with-exemplar reports scored at least as high as their ablated counterparts. Full article
(This article belongs to the Special Issue Nature-Based Solutions and Resilience of Infrastructure Systems)
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37 pages, 958 KB  
Review
Deep Learning-Based Surface Crack Detection in Bridge Structures: A Review
by Jia Li, Mustafasanie M. Yussof, Beiping Tian and Zhengrui Zhang
Infrastructures 2026, 11(7), 246; https://doi.org/10.3390/infrastructures11070246 - 20 Jul 2026
Viewed by 303
Abstract
Surface cracks pose a significant threat to the durability and operational safety of concrete bridges, making accurate crack detection essential for effective structural health monitoring. Conventional manual inspection is limited by low efficiency, subjective judgment, and safety risks during field operations. Although deep [...] Read more.
Surface cracks pose a significant threat to the durability and operational safety of concrete bridges, making accurate crack detection essential for effective structural health monitoring. Conventional manual inspection is limited by low efficiency, subjective judgment, and safety risks during field operations. Although deep learning computer vision techniques have become the dominant approach for automated crack detection, existing review studies provide limited discussion of their adaptability to different bridge structures and offer insufficient guidance for practical engineering applications. This review systematically summarizes recent advances in deep learning methods for surface crack detection in concrete bridges. Existing approaches are classified into crack classification, object detection, and semantic segmentation. The review further examines the relationships among bridge geometry, crack morphology, inspection conditions, and model performance. It also compares the technical challenges associated with beam, arch, cable stayed, and suspension bridges and discusses suitable model adaptation strategies for different structural characteristics. The analysis shows that bridge specific model architectures can significantly improve detection accuracy and robustness under complex inspection conditions. However, several challenges remain, including the limited availability of high quality datasets, data imbalance, inadequate detection of small cracks on curved surfaces under multiple viewing conditions, and the high cost of field deployment. This review provides practical guidance for selecting appropriate deep learning techniques for concrete bridge inspection and highlights future research directions toward integrating advanced sensing, multimodal data fusion, and intelligent inspection technologies to achieve more accurate, efficient, and reliable bridge condition assessment. Full article
(This article belongs to the Section Infrastructures and Structural Engineering)
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27 pages, 16284 KB  
Article
Experimental and Numerical Study on Local Buckling Analysis of Q550 High-Strength Steel T-Rib-Stiffened Plates
by Peng Chen, Qingtian Su, Yongcheng Lu, Sizhe Wang and Yanhong Chen
Infrastructures 2026, 11(7), 245; https://doi.org/10.3390/infrastructures11070245 - 20 Jul 2026
Viewed by 312
Abstract
In high-strength steel structures, local plate buckling is a critical concern due to the slenderness of plate elements. This study experimentally and numerically investigated the local buckling analysis of Q550 high-strength steel stiffened plates with T-ribs under compression. Five specimens with different base-plate [...] Read more.
In high-strength steel structures, local plate buckling is a critical concern due to the slenderness of plate elements. This study experimentally and numerically investigated the local buckling analysis of Q550 high-strength steel stiffened plates with T-ribs under compression. Five specimens with different base-plate slenderness were fabricated and subjected to uniaxial compression loading tests. The base plate of all specimens exhibited significant local out-of-plane deformation, and the T-ribs experienced varying degrees of in-plane and out-of-plane deformations. The buckling strength of the specimens decreases with increasing base-plate slenderness. The residual stresses and initial geometric imperfections of the high-strength steel stiffened plate were experimentally measured. Finite element (FE) models incorporating the residual stresses and initial geometric imperfections were established to simulate the buckling behavior of the stiffened plates. The FE models were validated against the experimental results and a parametric study was further conducted. The numerical results indicate that both residual stresses and initial geometric imperfections have a moderate influence on the buckling strength. Furthermore, the formulations in several national design codes used to account for local buckling effects were evaluated. The Eurocode provides approximate predictions, while the Chinese code and Japanese code provide conservative estimations. Full article
(This article belongs to the Special Issue Resilient and Sustainable Steel Structures: Advanced Manufacturing, Lifetime Extension and Disaster Mitigation)
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44 pages, 31279 KB  
Article
Multi-Hazard Assessment of Transportation Infrastructures Using an Integrated GIS–AHP–WLC Approach: Implementation in the CI-RES Platform
by Maurizio Pollino, Alberto Tofani and Gregorio D’Agostino
Infrastructures 2026, 11(7), 244; https://doi.org/10.3390/infrastructures11070244 - 18 Jul 2026
Viewed by 632
Abstract
Transportation infrastructure networks are increasingly exposed to multiple natural hazards, thus demanding sophisticated assessment methodologies for evaluating compound threats and supporting decision-making. This paper presents an operational multi-hazard assessment framework integrating the Analytical Hierarchy Process (AHP) with Weighted Linear Combination (WLC) within a [...] Read more.
Transportation infrastructure networks are increasingly exposed to multiple natural hazards, thus demanding sophisticated assessment methodologies for evaluating compound threats and supporting decision-making. This paper presents an operational multi-hazard assessment framework integrating the Analytical Hierarchy Process (AHP) with Weighted Linear Combination (WLC) within a GIS-based decision support system. The methodology is implemented as a plugin for CI-RES (critical infrastructure resilience), a web-based geospatial platform developed by ENEA for integrated analysis and resilience assessment of critical infrastructure systems, enabling automated evaluation of infrastructure elements against six natural hazards: seismic, flood, landslide, volcanic, wildfire, and tsunami. The approach combines spatial analysis with multi-criteria decision-making techniques, allowing users to define hazard priorities through pairwise comparison matrices while ensuring consistency through automatic validation procedures. A comprehensive case study covering the Italian national territory demonstrates the framework’s ability to process large-scale infrastructure datasets, generating spatially explicit hazard maps and statistical summaries. Our results reveal significant variations in multi-hazard exposure across different infrastructure types and geographic regions, with approximately 48% of the analysed road network falling within medium-to-high multi-hazard zones, 58% of road bridges and viaducts, 46% of railways, and 66% of railway bridges. The integration within the CI-RES platform provides stakeholders with an accessible web-based interface for conducting multi-hazard assessments, supporting evidence-based infrastructure planning and emergency management decisions. This work contributes both methodologically, through the AHP–WLC integration, and practically, through its implementation in an operational decision support system. Full article
(This article belongs to the Special Issue 10th Anniversary of Infrastructures—Recent Advances and Perspectives)
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30 pages, 5765 KB  
Article
Automated Spatiotemporal Tracking of Crack Evolution in Concrete Structures Using UAV and Point Clouds
by Xubin He, Xingjian Shi, Jiawang Song, Ling Yang, Xiaoming Hu, Yuanzhou Jiang, Haoxuan Weng, Yousong Zhang and Zhe Xia
Infrastructures 2026, 11(7), 243; https://doi.org/10.3390/infrastructures11070243 - 17 Jul 2026
Viewed by 336
Abstract
Spatiotemporal change detection of surface cracks in concrete structures is of great importance for evaluating and maintaining their structural health. The development of robotics and 3D computer vision technologies provides new solutions for key subtasks in this process, including automated data acquisition, spatial [...] Read more.
Spatiotemporal change detection of surface cracks in concrete structures is of great importance for evaluating and maintaining their structural health. The development of robotics and 3D computer vision technologies provides new solutions for key subtasks in this process, including automated data acquisition, spatial localization and quantification of cracks, and multi-temporal crack registration. This study proposes an automated UAV- and point cloud-based framework for detecting spatiotemporal changes in cracks in concrete structures. First, the proposed autonomous UAV path-planning algorithm is used to achieve data acquisition that conforms to complex structural geometries. Then, an improved SfM algorithm is employed to realize spatial crack localization and local point cloud densification. Finally, accurate registration of crack point clouds from different periods is achieved based on a two-step registration strategy. Experimental results on a real large-scale concrete structure show that the proposed path-planning algorithm can achieve complete envelope coverage conforming to the structural geometry, with an effective coverage ratio above 99.7%. The dimensional error of structural reconstruction is controlled within 20 mm. The average crack localization time is 4.00 s, and mean absolute error of crack width quantification is 0.44 mm. The average crack registration error is 0.97 mm, thereby enabling accurate tracking of crack evolution. Full article
(This article belongs to the Section Infrastructures Inspection and Maintenance)
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39 pages, 5575 KB  
Article
Hierarchical Obstacle-Avoidance Motion Planning Framework for a Road-Rail Dual-Use Bridge Inspection Manipulator
by Yong Zhang, Li Su, Linjie Li, Nan Song, Li Ba and Guobing Yan
Infrastructures 2026, 11(7), 242; https://doi.org/10.3390/infrastructures11070242 - 16 Jul 2026
Viewed by 286
Abstract
Under-bridge inspection involves complex structural geometries, confined working spaces, and substantial safety risks for manual operation. To address these challenges, this study proposes a hierarchical obstacle-avoidance motion-planning framework for a large road-rail dual-use bridge inspection manipulator. First, a consistent kinematic model is established [...] Read more.
Under-bridge inspection involves complex structural geometries, confined working spaces, and substantial safety risks for manual operation. To address these challenges, this study proposes a hierarchical obstacle-avoidance motion-planning framework for a large road-rail dual-use bridge inspection manipulator. First, a consistent kinematic model is established for an 11-DOF physical actuation system composed of six revolute joints and five prismatic telescopic joints. For inverse kinematics and template matching, the five physical telescopic joints are mapped to two equivalent prismatic variables, whereas collision checking and execution remain in the full physical joint space. Second, an improved bidirectional RRT-Connect planner is developed by integrating goal-biased sampling, multi-candidate expansion, soft low-lift constraints, and combined state and edge validity checking. Third, a pose-library-guided segmented planning strategy is introduced to reuse successful deployment sequences for known targets and to automatically generate intermediate poses for unseen targets. All post-processed trajectories are revalidated for collision and clearance before acceptance. Comparative simulations demonstrate that the proposed framework improves collision-free planning success and suppresses unreasonable high-lift configurations. The framework provides a reproducible planning solution for automated bridge inspection in confined under-bridge environments. Full article
(This article belongs to the Special Issue Advanced Technologies and Approaches for the Condition Monitoring of Railway Transportation Infrastructure)
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15 pages, 6534 KB  
Article
Research on the Cutting Efficiency of TBM Cutters in Jointed Rock Mass Based on a Multivariate Nonlinear Regression Model
by Pengfei Song, Bingquan Liu, Zhiwen Tan, Chengzhi Yi, Jia Shi, Xin Xiang, Yue Peng, Junning Xie, Junfeng Liu, Hongzhi Cui and Bolong Liu
Infrastructures 2026, 11(7), 241; https://doi.org/10.3390/infrastructures11070241 - 16 Jul 2026
Viewed by 267
Abstract
The factors influencing the cutting efficiency of tunnel boring machine (TBM) cutters in jointed rock masses are very complex. To investigate TBM disc cutter cutting performance under variable cutter spacing and penetration depth, Particle Flow Code (PFC) 2D discrete element numerical simulation is [...] Read more.
The factors influencing the cutting efficiency of tunnel boring machine (TBM) cutters in jointed rock masses are very complex. To investigate TBM disc cutter cutting performance under variable cutter spacing and penetration depth, Particle Flow Code (PFC) 2D discrete element numerical simulation is carried out on a granite jointed rock mass. The numerical model adopts a disc cutter tip angle of 20° and tip width of 12 mm, joint spacing of 5 mm, joint inclination angle of 45°, and lateral confining pressure of 2.5 MPa; cutter spacing is set to 60, 80, 100, 120 mm, and penetration depth ranges from 2 mm to 10 mm as research variables. The force chain distribution, jointed rock mass failure modes, penetration load and cutting efficiency of disc cutters under different working conditions are systematically analyzed. An indicator for measuring the cutting efficiency called “crack propagation specific energy” is proposed. Based on the numerical simulation results, a complete quadratic multivariate nonlinear regression model is established to predict cutting efficiency. The results show that the optimal cutting performance occurs at a cutter spacing of 80 mm, where the shear failure proportion of contact bonds and cutting efficiency simultaneously reach the maximum, while incomplete penetration of joint failure surfaces and small cutting areas appear under 60 mm and 120 mm cutter spacing. With the increase in the disc cutter penetration depth, the shear failure proportion of contact bonds rises continuously, and the number of tensile failure microcracks gradually decreases. The research outcomes can provide a theoretical reference for TBM shield tunnel construction parameter optimization. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence and Remote Sensing for Geohazard Modeling and Infrastructure Resilience)
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66 pages, 5978 KB  
Review
Reinforcement Learning for Optimizing Renewable Energy Utilization in Smart Grids: Recent Advances in Power Grids, Microgrids, and Building Energy Systems
by Panagiotis Michailidis, Federico Minelli, Hasan Huseyin Coban, Iakovos Michailidis and Elias Kosmatopoulos
Infrastructures 2026, 11(7), 240; https://doi.org/10.3390/infrastructures11070240 - 15 Jul 2026
Viewed by 709
Abstract
The extensive deployment of renewable energy sources (RES) across modern energy infrastructure has introduced significant operational complexity, necessitating the development of advanced data-driven control strategies to ensure reliable and efficient system operation. Among these approaches, reinforcement learning (RL) has emerged as a promising [...] Read more.
The extensive deployment of renewable energy sources (RES) across modern energy infrastructure has introduced significant operational complexity, necessitating the development of advanced data-driven control strategies to ensure reliable and efficient system operation. Among these approaches, reinforcement learning (RL) has emerged as a promising paradigm for managing renewable generation and coordinating interconnected energy subsystems under uncertainty and dynamic operating conditions. The current paper presents a comprehensive review of RL-based control applications across RES-integrated energy domains, including power grids, microgrids, and building energy systems. The paper begins by outlining the fundamental characteristics of these smart grid energy environments along with the mathematical foundations of RL and its principal algorithmic families. A structured analysis of recent peer-reviewed studies is then conducted, with the literature systematically categorized according to the corresponding energy domain. A high number of impactful selected studies are further examined across multiple key dimensions, including RL methodologies, agent architectures, reward design, baseline control strategies, RES-integrated technologies, and control objectives. Based on this multi-dimensional evaluation, the review identifies emerging trends and highlights dominant design patterns across power grid, microgrid, and building-level applications. Finally, the observations are critically discussed and future research directions are outlined towards the development of scalable, practical, and reliable RL-based energy management solutions for next-generation smart grid systems. Full article
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20 pages, 1355 KB  
Article
Spatial Patterns of Bridge Deterioration and Municipal Maintenance Potential for Municipality-Managed Bridges in the Chubu Region of Japan
by Saki Namimatsu
Infrastructures 2026, 11(7), 239; https://doi.org/10.3390/infrastructures11070239 - 15 Jul 2026
Viewed by 259
Abstract
Aging bridge infrastructure poses a growing challenge for Japanese municipalities under population decline, fiscal constraints, and shortages of technical personnel. This study analyzes municipality-managed bridges in the Chubu region of Japan by integrating bridge inspection records with demographic, fiscal, technical staffing, and regional-condition [...] Read more.
Aging bridge infrastructure poses a growing challenge for Japanese municipalities under population decline, fiscal constraints, and shortages of technical personnel. This study analyzes municipality-managed bridges in the Chubu region of Japan by integrating bridge inspection records with demographic, fiscal, technical staffing, and regional-condition indicators. Bridge deterioration severity for 237 municipalities was evaluated, and its spatial structure was examined using Global Moran’s I and local indicators of spatial association (LISA), which respectively indicate whether similar deterioration levels are regionally clustered across the study area and where local clusters or spatial outliers occur. The results showed significant positive spatial autocorrelation, indicating that deterioration is spatially clustered and locally heterogeneous. Municipal maintenance potential was then represented through principal component analysis and classified by cluster analysis, identifying seven municipal types characterized by different combinations of depopulation, wide-area management burden, land-use conditions, and fiscal constraints. By integrating deterioration severity, LISA composition, and maintenance-potential typologies, the study distinguished municipalities where low deterioration is spatially stable, municipalities where highly deteriorated areas are locally concentrated, and municipalities where deterioration is high but spatially dispersed. These findings provide a basis for targeted support and strategic bridge management. Full article
(This article belongs to the Special Issue Advanced Technologies for Bridge Health Monitoring)
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20 pages, 1588 KB  
Article
Incorporating Increased Road User Costs into Pavement Management Modeling: A Case Study of Two-Lane Rural Highways
by Khaled A. Abaza and Mohamed S. Yamany
Infrastructures 2026, 11(7), 238; https://doi.org/10.3390/infrastructures11070238 - 13 Jul 2026
Viewed by 326
Abstract
The impact of increased road user costs on optimal pavement rehabilitation plans has been investigated using a simplified pavement management model. The increased road user costs include elevated vehicle operating costs (VOCs) due to work-zone lane closures and traveling on severely deteriorated pavements. [...] Read more.
The impact of increased road user costs on optimal pavement rehabilitation plans has been investigated using a simplified pavement management model. The increased road user costs include elevated vehicle operating costs (VOCs) due to work-zone lane closures and traveling on severely deteriorated pavements. A model is proposed for estimating the VOC for work-zone lane closures on two-lane rural highways, considering both stopping and idling costs. Another model for approximating the increased VOC associated with driving on poor/bad pavements is suggested as a function of the relevant VOC rate and a proportionality factor. Sample results are presented for a two-lane rural pavement network comprising 54.2 lane-kilometers. The sample optimal rehabilitation plans derived, excluding increased VOC, are associated with substantially higher VOC due to driving on severely deteriorated pavements. The inclusion of increased VOC because of badly damaged pavements has resulted in an improved pavement network without incurring extra expenses for highway agencies. The annual budget required to eliminate the VOC resulting from severely damaged pavements is $2.5 million while neglecting increased VOC, which decreases to $1.5 million when accounting for increased VOC. The incorporation of increased VOC has shifted fund allocation more towards substandard pavements. The optimal rehabilitation plan is associated with a $1.0 million annual budget, yielding a maximum road user saving of $0.411 million. Full article
(This article belongs to the Special Issue AI in Sustainable and Resilient Infrastructures: Construction, Management, and Maintenance)
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27 pages, 15247 KB  
Article
Evaluation of the Seismic Behavior of Existing Spillway Piers Using Incremental Dynamic Analysis: Applicability of Nonlinear Analytical Models to Piers Reinforced with Round Rebars and Low Rebar Ratios
by Yoshiki Matsuoka, Takenori Araki, Hiroshi Nakajima, Yasuyuki Nakanishi, Satoshi Uchida and Hikaru Nakamura
Infrastructures 2026, 11(7), 237; https://doi.org/10.3390/infrastructures11070237 - 13 Jul 2026
Viewed by 514
Abstract
Existing spillway piers constructed more than 60 years ago in Japan are usually reinforced with round rebars and have very low rebar ratios; consequently, their seismic response may be strongly influenced by post-cracking bond–slip. However, the applicability of nonlinear analytical models to such [...] Read more.
Existing spillway piers constructed more than 60 years ago in Japan are usually reinforced with round rebars and have very low rebar ratios; consequently, their seismic response may be strongly influenced by post-cracking bond–slip. However, the applicability of nonlinear analytical models to such piers has not yet been systematically clarified. In this study, practical modeling strategies for existing spillway piers were investigated by performing Incremental Dynamic Analysis (IDA) using both a beam model based on nonlinear moment–curvature (M-φ) relationships and a 3D finite element analysis that explicitly accounts for bond–slip between concrete and rebar. An actual spillway pier was analyzed at multiple seismic intensity levels, and the effects of bar diameter, rebar ratio, and bond condition were examined via 3D finite element analysis. The results showed that the beam model is useful for global screening but may misclassify the damage mode because it cannot explicitly represent bond–slip. By contrast, the 3D finite element analysis reproduced flexure-dominant damage patterns and quantified the influence of bond–slip on maximum displacement, residual displacement, rebar strain, tensile damage distribution, and compression damage distribution. These differences became more pronounced under stronger ground motions and for larger bar diameters and lower rebar ratios. The findings support a staged strategy for seismic performance evaluation that combines beam models for global screening with 3D finite element analysis for detailed member-level assessment. Full article
(This article belongs to the Section Infrastructures and Structural Engineering)
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22 pages, 4393 KB  
Article
Wind-Induced Response of Coupled Shear Wall Systems Based on Wind Tunnel Testing
by Sarah Bashour, Bassam Hwaija, Fadwa Issa, Firas Al Mahmoud and George Wardeh
Infrastructures 2026, 11(7), 236; https://doi.org/10.3390/infrastructures11070236 - 13 Jul 2026
Viewed by 345
Abstract
Coupled shear wall systems are widely used in tall buildings due to their high lateral stiffness and effectiveness in controlling wind-induced serviceability responses. Reliable assessment of their behavior under realistic wind loading requires accurate load representation and properly calibrated numerical modeling. This study [...] Read more.
Coupled shear wall systems are widely used in tall buildings due to their high lateral stiffness and effectiveness in controlling wind-induced serviceability responses. Reliable assessment of their behavior under realistic wind loading requires accurate load representation and properly calibrated numerical modeling. This study investigates the performance of coupled shear wall systems under wind loads derived from wind tunnel testing, where surface pressure time histories were extracted from the TPU Aerodynamic Database and used to generate equivalent full-scale, time-varying wind loads. The preliminary design of a 20-story building was established in ETABS based on current design codes. Detailed nonlinear time-history analyses were subsequently performed in OpenSees to assess the performance under three different wind hazards for the original and refined design, where shear walls were modeled using the Multiple-Vertical-Line-Element Model (MVLEM). Several wind demand indicators, including drift ratios, floor accelerations, and component and cladding performance were evaluated. The results demonstrate that the performance-based design framework enables the identification of critical vulnerabilities that may not be fully captured by conventional code-based drift and strength checks, particularly regarding localized damage accumulation and serviceability-related demands. Additionally, the refined configuration, while requiring only a 4.33% increase in total baseline concrete volume, effectively reduced peak roof drift by 60%, allowing reliable control of dynamic behavior and prevention of structural yielding, as well as ensuring the maintenance of both structural integrity and operational serviceability for the investigated high-rise configuration during severe wind events. Nevertheless, the findings are limited to the investigated building configuration and aerodynamic conditions considered in this study. Full article
(This article belongs to the Special Issue Earthquake and Multi-Hazard Resilience: Community-Level Insights and AI/ML Applications)
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27 pages, 15510 KB  
Article
A Vision-Based Quality Inspection Method for Embedded Rebar in High Piers Under Long-Range Imaging Conditions
by Dapeng Hui, Bin Xing, Sihao Zhang, Haibin Huang and Dong Liang
Infrastructures 2026, 11(7), 235; https://doi.org/10.3390/infrastructures11070235 - 13 Jul 2026
Viewed by 352
Abstract
In high-pier bridge construction, the quality and accuracy of embedded rebar placement are critical to ensuring structural safety and durability. However, conventional manual inspection methods are inefficient, subjective and pose significant safety risks in high-altitude operations. These methods are unable to comprehensively inspect [...] Read more.
In high-pier bridge construction, the quality and accuracy of embedded rebar placement are critical to ensuring structural safety and durability. However, conventional manual inspection methods are inefficient, subjective and pose significant safety risks in high-altitude operations. These methods are unable to comprehensively inspect all pier columns on a daily basis, and frequently result in delays in acceptance that necessitate rework. In order to address these challenges, the current study proposes a smart vision-based inspection framework for the automatic and high-precision quality assessment of rebar under long-distance imaging conditions. This approach allows quality inspectors to remotely predict and evaluate the embedment quality of rebars from a safe distance. Notably, this work introduces a novel dual-source coordinate fusion mechanism that integrates improved instance segmentation with corner detection for global-to-local precision enhancement, representing an original contribution to rebar placement inspection in complex high-pier scenarios. The framework integrates an improved YOLOv8-CD segmentation model and a corner detection algorithm through a dual-source coordinate fusion mechanism, achieving an integration of global rebar detection and local feature enhancement. The YOLOv8-CD model, when optimised, features the Convolutional Block Attention Module (CBAM) integrated into the backbone, with the objective of enhancing recognition accuracy for small targets. Additionally, a Dilation-Wise Residual (DWR) module has been inserted before the neck C2f layer for the purpose of strengthening multi-scale feature extraction. The process of perspective correction and pixel-to-actual-length conversion coefficienting is performed in order to achieve a millimetre-level measurement of the rebar spacing and diameter. Empirical validation through real high-pier construction scenes demonstrates that the proposed framework attains a detection accuracy of 98.82%, surpassing conventional YOLO-based and single-source methodologies. The experimental results demonstrate that this framework is able to detect objects at longer distances, and to maintain its performance when the target is at a greater distance than that which was used for training. The proposed approach is expected to provide an efficient, safe, and quantitative solution for intelligent bridge construction quality monitoring, offering valuable insights for the future development of smart construction and structural health inspection systems. Full article
(This article belongs to the Special Issue Sustainable Road Infrastructure: Safety, Performance and Resilience)
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22 pages, 4270 KB  
Article
Influence of Silt Physical Properties Under Pile Cap on Bearing Capacity of NT-CEP Pile Foundations
by Yongmei Qian, Bingyi Liu, Jialiang Liu, Yingtao Zhang, Yuchen Song and Ming Guan
Infrastructures 2026, 11(7), 234; https://doi.org/10.3390/infrastructures11070234 - 10 Jul 2026
Viewed by 373
Abstract
To clarify the poorly understood soil-structure interactions flanking the pile cap, this study systematically investigates the sensitivity of the New Type Concrete Expanded-Plate (NT-CEP) pile system to variations in sub-cap silt profiles, specifically moisture content (12%~16%) and dry density (80%~90% compaction degree). Mechanical [...] Read more.
To clarify the poorly understood soil-structure interactions flanking the pile cap, this study systematically investigates the sensitivity of the New Type Concrete Expanded-Plate (NT-CEP) pile system to variations in sub-cap silt profiles, specifically moisture content (12%~16%) and dry density (80%~90% compaction degree). Mechanical results indicate that the pile cap and expanded bearing plates operate via a robust synergistic load-sharing mechanism, with plastic failure zones localized beneath these components. Within conventional physical limits, fluctuations in moisture and density trigger less than a 4% variance in the ultimate compressive capacity, demonstrating the remarkable structural resilience of the internal compensatory load-transfer path. Based on the evaluated boundary conditions, a site-specific operational envelope featuring a minimum compaction degree of 80% and a critical moisture threshold below 14% is recommended as a preliminary reference. Nevertheless, explicit mechanical limitations must be rigorously addressed: these quantitative thresholds are strictly benchmarked against the scaled model testing utilizing a specific silt thickness and pile geometric stiffness ratio. Significant deviations in these parameters are expected under three distinct boundary constraints: (1) altered multi-axial stress paths inherent to complex interbedded geologies; (2) catastrophic matric suction loss and pore pressure accumulation driven by elevated groundwater tables; and (3) severe skin friction degradation common in thixotropic soft clays. Consequently, these indicators constitute a context-specific design envelope rather than a rigid universal standard, providing a mechanics-driven baseline for the gradient optimization of advanced NT-CEP foundations while delineating required calibration paths for future full-scale field instrumentation. Full article
(This article belongs to the Topic Sustainable Structural Methods for Construction and Infrastructure)
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16 pages, 2776 KB  
Article
Laboratory Evaluation of Asphalt Mixes of High Reclaimed Asphalt Pavement Contents with Polymer Cool Mix Additive and Rejuvenator as Sustainable Paving Materials
by Cody Hall, Giuseppe Gianforte and Hosin (David) Lee
Infrastructures 2026, 11(7), 233; https://doi.org/10.3390/infrastructures11070233 - 10 Jul 2026
Viewed by 365
Abstract
The use of reclaimed asphalt pavement (RAP) has been increasing due to its economic benefits and environmental sustainability. Adding RAP materials introduces age-hardened binder, which tends to increase the rutting resistance but decrease cracking resistance. This study aims to evaluate the effects of [...] Read more.
The use of reclaimed asphalt pavement (RAP) has been increasing due to its economic benefits and environmental sustainability. Adding RAP materials introduces age-hardened binder, which tends to increase the rutting resistance but decrease cracking resistance. This study aims to evaluate the effects of various RAP contents and binder additives on asphalt performance using the Hamburg wheel tracking test, the Semi-Circular Bending-Illinois Flexibility Index Test (SCB-IFIT) and the Indirect Tensile Asphalt Cracking Test (IDEAL-CT). Asphalt mixtures with RAP contents of 0%, 20%, 30%, 40%, and 50% were prepared using two different binder additives of Zero-M polymer cool mix asphalt additive (PCMA) and Anova vegetable oil-based rejuvenator. Based on the Hamburg test results, the rutting resistance significantly increased by adding 20% RAP but did not increase the rutting resistance any further when RAP increased from 20% to 50%. However, the increase in RAP content exhibited a negative impact on cracking resistance by lowering Flexibility Index (FI) based on the SCB-IF test by 50% or more and CT Index (CTindex) based on IDEAL-CT test by 60% or more. For each RAP content, asphalt mixtures incorporating two different additives were tested: (1) Zero-M additive at a dosage rate of 10% of the total binder with mixing/compaction temperature of 110 °C and (2) Anova additive at a dosage rate of 5% of the RAP binder with mixing/compaction temperature of 135 °C. Compared to the control specimens without additive, asphalt mixtures with Zero-M additive increased FI and CTindex by 50% except CTindex of 30% RAP mix. Zero-M additive increased the rut depth from 3 mm to 10 mm for 20% and 30% RAP mixes but, for 40% and 50% RAP contents, the rutting was less than 5 mm after 20,000 repetitions. Anova rejuvenator did not increase FI and CTindex of 30% and 50% RAP mixes but increased FI and CTindex by 50% for 40% RAP mix. Anova additive did not increase the rutting of the control mix. The SCB-IFIT test results exhibited an average coefficient of variation (COV) of 0.25 whereas the IDEAL-CT test results had a COV of 0.20. The IDEAL-CT test, with its simpler preparation process and more consistent results, is recommended as the preferred test procedure over the SCB-IFIT test. Full article
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21 pages, 1304 KB  
Article
Revisiting Historical Design Methods for the Rapid Structural Analysis of Existing Masonry Tunnel Linings
by Erica Lenticchia
Infrastructures 2026, 11(7), 232; https://doi.org/10.3390/infrastructures11070232 - 8 Jul 2026
Viewed by 350
Abstract
Masonry tunnels built between late 19th and early 20th century constitute a widespread asset of the existing infrastructure network and currently require systematic condition assessment, monitoring, and maintenance interventions. Despite some existing regulatory frameworks, performing detailed assessments on tunnels with masonry linings remains [...] Read more.
Masonry tunnels built between late 19th and early 20th century constitute a widespread asset of the existing infrastructure network and currently require systematic condition assessment, monitoring, and maintenance interventions. Despite some existing regulatory frameworks, performing detailed assessments on tunnels with masonry linings remains a difficult task due to the significant uncertainties and the complex behavior of masonry. To address this gap, this work proposes a Simplified Approach (SA) for the structural assessment of masonry tunnels. A formulation adapted from classic static methods is proposed for the rapid assessment of the structural capacity of masonry linings. The proposed approach was applied and evaluated through a well-documented case study, in which the actual stress states were obtained with on-site measurements, that were employed to calibrate the model parameters by means of best fitting. The SA was employed to conduct a detailed stress verification along the entire lining, demonstrating its effectiveness as a calibrated tool for the large-scale safety assessment of historical tunnels, for the identification of critical sections that may require subsequent non-linear Finite Element Method analysis for Ultimate Limit State verification. Full article
(This article belongs to the Section Infrastructures and Structural Engineering)
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35 pages, 5884 KB  
Article
Microstructure and Drying Shrinkage of Cement Mortars Containing High-Volume Fly Ash and Glass Waste Nanoparticles
by Ghasan Fahim Huseien, Akram M. Mhaya, Waiching Tang, Masoumeh Khamehchi and Jahangir Mirza
Infrastructures 2026, 11(7), 231; https://doi.org/10.3390/infrastructures11070231 - 4 Jul 2026
Viewed by 695
Abstract
Replacing Ordinary Portland Cement (OPC) with high volumes of fly ash (FA) offers a practical approach to reducing the environmental impacts associated with cement manufacturing and landfill disposal. However, high FA replacement levels, particularly up to 60%, often lead to lower early-age strength. [...] Read more.
Replacing Ordinary Portland Cement (OPC) with high volumes of fly ash (FA) offers a practical approach to reducing the environmental impacts associated with cement manufacturing and landfill disposal. However, high FA replacement levels, particularly up to 60%, often lead to lower early-age strength. This study developed a green cement mortar containing 60% FA and waste bottle glass nanoparticles (WBGNPs). The WBGNPs were incorporated at replacement levels of 2%, 4%, 6%, 8%, and 10% by volume of the OPC–FA binder. The findings showed that the addition of 4–6% WBGNPs significantly promoted the formation of dense reaction gels and enhanced compressive strength by 12.5–39.1%. Similar performance trends were observed in both the engineering and microstructural properties. The combined incorporation of FA and WBGNPs also improved drying shrinkage performance by reducing capillary stresses during water evaporation and minimizing crack development within the cement matrix. Additionally, a proposed shrinkage prediction model was validated using experimental data and demonstrated good agreement, with an average prediction error of approximately 8%. Overall, the incorporation of WBGNPs provides an effective method for producing high-volume FA cement mortars with satisfactory engineering properties suitable for concrete applications in tropical environments. This approach further supports sustainability by reducing waste generation, lowering landfill demand, and minimizing environmental pollution. Full article
(This article belongs to the Special Issue Recent Advances in Enhancing Sustainability and Durability of Cement and Concrete)
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26 pages, 15986 KB  
Article
Performance-Based Redesign of a High-RAP Half-Warm Recycled Asphalt Mixture with Foamed Bitumen
by Caroline F. N. Moura, Nuno M. F. Araújo, Hugo M. R. D. Silva and Joel R. M. Oliveira
Infrastructures 2026, 11(7), 230; https://doi.org/10.3390/infrastructures11070230 - 4 Jul 2026
Viewed by 355
Abstract
The development of recycled asphalt mixtures combining reduced production temperatures with adequate mechanical performance remains challenging in circular pavement engineering. This study assessed the performance-based redesign of a half-warm mix asphalt (HWMA) produced at approximately 90 °C with a very high reclaimed asphalt [...] Read more.
The development of recycled asphalt mixtures combining reduced production temperatures with adequate mechanical performance remains challenging in circular pavement engineering. This study assessed the performance-based redesign of a half-warm mix asphalt (HWMA) produced at approximately 90 °C with a very high reclaimed asphalt pavement (RAP) content and foamed bitumen, using previously validated cold recycled mixture (CRM) and hot recycled mix asphalt (HRMA) formulations as contextual benchmarks. An initial CRM-derived HWMA was evaluated to assess whether cold-recycling design logic could be transferred to half-warm production without added water or cement. Although the mixture showed satisfactory volumetric and moisture-related responses, wheel tracking identified rutting as the governing limitation. The mixture was redesigned by incorporating coarse steel slag aggregate (SSA) to correct the aggregate size distribution, reducing filler content and adjusting the added foamed bitumen while maintaining RAP and SSA at 98% of the aggregate skeleton. The combined redesign reduced the wheel-tracking slope in air from 1.25 to 0.32 mm/103 cycles and the proportional rut depth in air from 28.1% to 10.4%. Nevertheless, the redesigned HWMA remained less rut-resistant than both benchmarks, confirming the need for further optimisation. It achieved stiffness close to the HRMA benchmark and a fatigue response compatible with base-layer application, although moisture durability requires further validation. Overall, the study demonstrates the feasibility of a sequential performance-based redesign approach for high-RAP HWMA while highlighting the need for systematic optimisation and field validation before broader implementation. Full article
(This article belongs to the Special Issue Innovative Solutions for Resilient, Sustainable and Durable Infrastructure Systems)
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29 pages, 6258 KB  
Article
Calibrating an Improved I-Effective Method for Prestressed Concrete Beams Strengthened with FRP
by Kimberly Waggle Kramer and Hayder A. Rasheed
Infrastructures 2026, 11(7), 229; https://doi.org/10.3390/infrastructures11070229 - 4 Jul 2026
Viewed by 282
Abstract
The deflection of prestressed (pretensioned) concrete members strengthened with FRP requires a comprehensive evaluation. An extensive parametric study is performed using a rigorous analysis procedure based on a trilinear moment-curvature approach. There are 8100 pretensioned concrete beams analyzed by varying the cross-section dimensions, [...] Read more.
The deflection of prestressed (pretensioned) concrete members strengthened with FRP requires a comprehensive evaluation. An extensive parametric study is performed using a rigorous analysis procedure based on a trilinear moment-curvature approach. There are 8100 pretensioned concrete beams analyzed by varying the cross-section dimensions, span length-to-depth ratio, shear span-to-span ratio, concrete compressive strength, prestressing reinforcement ratio, FRP strengthening ratio and FRP material properties. It was determined that the normalized effective moment of inertia at first yielding is statistically correlated with the normalized cracked moment of inertia, with an almost-perfect regression (R2 = 0.9886). It was further found that when postulating the inverse of the effective moment of inertia in terms of a parabolic function of the beam maximum moment, the deflections of the cracked beam agree closely with experimental deflections. Boundary conditions for that equation are applied at the cracking and prestress-yielding points. Ultimately, it was realized that the immediate deflection predictions based on the modified beam effective moment of inertia expression proposed yield reliable deflection estimates for cracked prestressed members externally strengthened with FRP, compared with experimental results and other analytical predictions. Full article
(This article belongs to the Special Issue Advances in Reinforced Concrete Infrastructure: Enhancing Structural Resilience and Promoting Sustainability, 2nd Edition)
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50 pages, 11970 KB  
Review
Recent Advances in AI and Signal Processing for PZT-Based Structural Health Monitoring
by Reza Soleimanpour
Infrastructures 2026, 11(7), 228; https://doi.org/10.3390/infrastructures11070228 - 4 Jul 2026
Viewed by 523
Abstract
Structural health monitoring (SHM) systems fundamentally rely on effective sensing technologies for reliable damage detection and structural condition assessment. Among the available sensing approaches, piezoelectric (PZT)-based transducers are widely used in civil engineering due to their dual actuation–sensing capability, high sensitivity, low cost, [...] Read more.
Structural health monitoring (SHM) systems fundamentally rely on effective sensing technologies for reliable damage detection and structural condition assessment. Among the available sensing approaches, piezoelectric (PZT)-based transducers are widely used in civil engineering due to their dual actuation–sensing capability, high sensitivity, low cost, and suitability for real-time monitoring. However, SHM performance not only depends on the sensing hardware, but also on the signal processing techniques that extract meaningful damage-related information from measured responses. Recently, Artificial Intelligence (AI), particularly machine learning (ML) and deep learning (DL), has shown strong potential to enhance automation and improve the performance of SHM systems. This paper provides a critical review of signal processing and data-driven learning approaches for PZT-based guided-wave (GW) SHM and nondestructive testing (NDT), with applications to metallic, composite, and concrete structures. The review covers developments from early ML-based GW SHM methods to recent advances in DL, hybrid frameworks, and physics-informed approaches. Although emphasis is placed on civil infrastructure, developments in other fields such as aerospace and energy engineering are also reviewed due to their role in validating GW-based SHM methodologies. The fundamental theory of PZT sensing and guided wave propagation is introduced to establish the required background for monitoring techniques. Classical signal processing methods are then reviewed, followed by AI-based SHM frameworks, with particular emphasis on hybrid approaches that integrate physics-based signal processing with data-driven models to improve robustness, accuracy, and generalization. Key challenges such as environmental variability, sensor degradation, limited labeled data, and model transferability are discussed, along with future research directions including physics-informed machine learning (PIML), transfer learning, explainable AI, and baseline-free SHM. The review highlights that hybrid and physics-informed frameworks offer strong potential for field deployment by improving robustness, reducing data dependency, and enhancing generalization capability. A key contribution of this work is the comparative synthesis of signal processing, ML, DL, and hybrid methodologies across different material systems and structural types, together with a structured discussion of the challenges and future research directions for real-world implementation. Full article
(This article belongs to the Special Issue Advanced Technologies for Civil Infrastructure Monitoring)
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21 pages, 2294 KB  
Article
Application of Artificial Intelligence in River Pollution Monitoring for Environmental Management Support and Impact Mitigation
by Jullia Fernandes Felizardo and Thabatta Moreira Alves de Araújo
Infrastructures 2026, 11(7), 227; https://doi.org/10.3390/infrastructures11070227 - 4 Jul 2026
Viewed by 319
Abstract
Visible solid waste pollution in water bodies has intensified, threatening ecosystems and public health. This study proposes a low-complexity approach based on Convolutional Neural Networks (CNNs) for the automatic detection of visible litter in rivers using images. The methodology involves a curated dataset [...] Read more.
Visible solid waste pollution in water bodies has intensified, threatening ecosystems and public health. This study proposes a low-complexity approach based on Convolutional Neural Networks (CNNs) for the automatic detection of visible litter in rivers using images. The methodology involves a curated dataset from multiple sources and the application of Transfer Learning with the MobileNetV2 architecture, chosen for its computational efficiency. The model achieved stable performance across 20 independent runs, with an average test accuracy of 89.7%, an F1-score of 90.9%, and an AUC of 0.996. Notably, the representative model selected for qualitative illustration produced zero false negatives on the test set; this result reflects the behavior of a specific model instance and should be interpreted as an illustrative outcome rather than the guaranteed operational performance of the method. The results indicate that the proposed approach is a viable, scalable, and accessible technological alternative for automated river monitoring. Future integration with web applications and geospatial platforms could enhance its utility for environmental agencies and public managers. Full article
(This article belongs to the Special Issue Computational Methods in Engineering)
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26 pages, 65548 KB  
Article
Effect of Barrier Location on Debris Flow in a Watershed in Chosica, Peru
by Marco Herber Muñiz and Doris Esenarro
Infrastructures 2026, 11(7), 226; https://doi.org/10.3390/infrastructures11070226 - 1 Jul 2026
Viewed by 454
Abstract
This study addresses the impact of the location of transverse barriers on debris flow in the Libertad sub-basin, in Chosica, Peru. Intense seasonal rainfall in this region causes destructive flows that threaten infrastructure and human lives. Using geographic information system tools, hydrological models [...] Read more.
This study addresses the impact of the location of transverse barriers on debris flow in the Libertad sub-basin, in Chosica, Peru. Intense seasonal rainfall in this region causes destructive flows that threaten infrastructure and human lives. Using geographic information system tools, hydrological models and hydraulic simulations, scenarios with barriers installed at different distances from the debris source were evaluated. The results indicate that the barrier located closest to the source (0.3L) is the most effective, achieving a reduction in velocity of 12.9% at the most critical urban monitoring point, the greatest volume retention capacity (790.02 m3), and the greatest decrease in flow escaping from the study area (65.7%). In contrast, barriers at 0.5L, 0.7L, and 0.9L show progressively lower effectiveness. This finding highlights the importance of a strategic design that optimises the position of the barriers according to the geomorphological and hydrological characteristics of the area. It is concluded that an adequate distribution of barriers, complemented with integrated watershed management strategies, can considerably mitigate the risks associated with debris flows in vulnerable urban areas. Full article
(This article belongs to the Special Issue Advanced Technologies for Climate Resilient Infrastructures)
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