Infrastructures

30 pages, 6786 KB

Open AccessArticle

Integration of Renewable Energy Sources with Hybrid Power Quality Conditioners in Co-Phase Traction Systems for Electric Railways

by Sajjad Najafpour, Yasaman Darvishpour, S. Mohammad Mousavi G., Hamed Jafari Kaleybar, Morris Brenna and Vahid Kamrani

Infrastructures 2026, 11(9), 314; https://doi.org/10.3390/infrastructures11090314 (registering DOI) - 6 Sep 2026

Abstract

The increasing demand for electrified rail transportation has intensified power quality (PQ) challenges, including harmonics, voltage imbalance, and low power factor (PF). These issues have driven the development of advanced traction power supply systems, particularly co-phase configurations, to improve power quality, enhance grid-connected [...] Read more.

The increasing demand for electrified rail transportation has intensified power quality (PQ) challenges, including harmonics, voltage imbalance, and low power factor (PF). These issues have driven the development of advanced traction power supply systems, particularly co-phase configurations, to improve power quality, enhance grid-connected stability, and strengthen the operational resilience of railway power infrastructure. This paper proposes a co-phase power supply system for high-speed railways that facilitates high-speed train operation by integrating power quality compensation technologies while reducing the required number of neutral sections by half, thereby improving the continuity and robustness of traction power delivery. To address PQ issues, a capacitive-coupled hybrid power quality conditioner (HPQC) incorporating renewable energy sources (RESs) into its DC link is introduced. Given the highly dynamic and time-varying nature of railway loads, a sliding mode control (SMC)-based robust control method is developed based on the state space model of the co-phase power supply system and the HPQC to provide a stable and rapid response to load variations and operational disturbances. The effectiveness and real-time implementation capability of the proposed approach are validated through real-time control hardware-in-the-loop (CHIL) simulations. Results from MATLAB/Simulink simulations and real-time CHIL testing demonstrate substantial harmonic reduction, improved power factor, reduced negative-sequence currents, and enhanced overall system efficiency. These outcomes confirm the suitability of the proposed system for modern high-speed railway applications and highlight its contribution to resilient traction power supply systems capable of maintaining reliable operation under highly variable loading conditions. Full article

(This article belongs to the Special Issue The Resilience of Railway Networks: Enhancing Safety and Robustness)

30 pages, 12002 KB

Open AccessArticle

ANN-Based Surrogate Modeling for Seismic Fragility Assessment of Double-Layer Barrel Vault Roofs Supported by Double-Layer Latticed Walls

by Mohammad Kheirollahi, Moein Mirzaei, Seyed Amir Banimahd, Shaghayegh Karimzadeh, Nuno Mendes and Paulo B. Lourenço

Infrastructures 2026, 11(9), 313; https://doi.org/10.3390/infrastructures11090313 - 4 Sep 2026

Abstract

Double-layer barrel vault roofs with double-layer vertical walls are widely used in important public buildings because of their high structural efficiency, favorable stiffness-to-weight ratio, and architectural versatility. Although incremental dynamic analysis (IDA) is a widely accepted approach for seismic assessment, it requires numerous [...] Read more.

Double-layer barrel vault roofs with double-layer vertical walls are widely used in important public buildings because of their high structural efficiency, favorable stiffness-to-weight ratio, and architectural versatility. Although incremental dynamic analysis (IDA) is a widely accepted approach for seismic assessment, it requires numerous nonlinear time-history analyses (NTHAs), resulting in high computational cost. This study presents an artificial neural network (ANN)-based surrogate modeling framework to accurately predict the seismic response of these structural systems, reducing the need for repeated NTHAs, enabling rapid estimation of structural dynamic responses, and facilitating direct development of seismic fragility curves. The proposed framework substantially decreases computational effort while maintaining an effective balance between accuracy and efficiency. A comprehensive seismic damage database is first generated using finite element (FE) models developed in OpenSees. Fragility curves are then obtained using both the conventional IDA procedure and the proposed ANN-based surrogate approach. Results show that the ANN surrogate accurately predicts the responses of structures subjected to scaled ground motions and effectively captures their nonlinear seismic behavior. Furthermore, the resulting fragility curves closely match those from the conventional IDA method, demonstrating the accuracy, reliability, and efficiency of the proposed framework for rapid seismic assessment of double-layer barrel vault structures with double-layer walls. Full article

(This article belongs to the Special Issue Smart and Durable Inorganic-Matrix Composite Systems for Sustainable Infrastructure Rehabilitation)

35 pages, 11307 KB

Open AccessArticle

Pantograph Arc Detection for Condition Monitoring of 3-kV DC Railway Infrastructure

by Palesa H. Kubayi and Bonginkosi A. Thango

Infrastructures 2026, 11(9), 312; https://doi.org/10.3390/infrastructures11090312 - 3 Sep 2026

Abstract

Pantograph arcing is both a vehicle current-collection problem and a railway-infrastructure condition-monitoring problem because repeated loss of electrical contact can accelerate wear of the overhead contact wire and pantograph strip, degrade traction power quality, and increase maintenance demand. This study develops a leakage-safe [...] Read more.

Pantograph arcing is both a vehicle current-collection problem and a railway-infrastructure condition-monitoring problem because repeated loss of electrical contact can accelerate wear of the overhead contact wire and pantograph strip, degrade traction power quality, and increase maintenance demand. This study develops a leakage-safe diagnostic framework for 3-kV DC railway operation using 13 independent high-frequency recordings from the public Trenitalia E464 pantograph-arcing dataset. Because the repository does not provide synchronized optical/contact-force ground truth, the machine-learning target is consistently treated as a physics-guided candidate interval rather than an independently verified arc label. Pantograph voltage, pantograph current, filter voltage, and braking-rheostat current were sampled at 50 kSa/s and transformed into 207 event-preserving analysis windows. A total of 849 candidate features were extracted across time, frequency, time-frequency, nonlinear, and physics-informed electrical domains. The strongest leave-one-recording-out configuration was Extra Trees with frequency-domain features, with mean event-level accuracy of 0.9936, balanced accuracy of 0.9952, Macro-F1 of 0.9932, MCC of 0.9874, ROC-AUC of 0.9994, and PR-AUC of 0.9989. Ten-repeat grouped five-fold validation, with complete recordings retained as groups, produced a mean Macro-F1 of 0.9910 (SD 0.0193) across 50 grouped test folds. Five hundred recording-grouped bootstrap resamples yielded a Macro-F1 mean of 0.9893 with a 95% confidence interval of 0.9694–1.0000. A dedicated guard audit found zero candidate-interval overlap in all 138 retained normal 100 ms feature windows. Sensitivity analysis showed that 100 ms spectral features were materially more stable than 20 ms features, while 25% and 50% candidate-overlap thresholds produced nearly identical performance. The dataset does not contain long-duration independently verified arc-free operation, chainage/GPS catenary position, or synchronized contact-force measurements; consequently, the results are interpreted as proof-of-concept electrical screening of candidate current-collection disturbances rather than fleet-wide ground-truth arc detection. Full article

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34 pages, 14895 KB

Open AccessArticle

The 2025 Construction-Stage Collapse of the Jianzha Yellow River Super Bridge: A Document-Based Forensic Engineering Synthesis

by Oğuzhan Çetindemir

Infrastructures 2026, 11(9), 311; https://doi.org/10.3390/infrastructures11090311 - 2 Sep 2026

Abstract

On 22 August 2025, a partially erected section of the Jianzha Yellow River Super Bridge collapsed during cable-tensioning operations, causing 13 fatalities and leaving 3 persons missing. The bridge was being designed as a 366 m main-span continuous steel truss arch, which was [...] Read more.

On 22 August 2025, a partially erected section of the Jianzha Yellow River Super Bridge collapsed during cable-tensioning operations, causing 13 fatalities and leaving 3 persons missing. The bridge was being designed as a 366 m main-span continuous steel truss arch, which was erected via cantilever construction supported by a temporary cable-supported fastening system. This document-based forensic engineering synthesis uses the published official investigation report and independently accessed design and technical literature. No independent site inspection, component examination, raw data review, original video review, or validated nonlinear collapse reconstruction was undertaken; the exact instant and progressive development of splice failure are not visible in the report-reproduced chronology and remain unresolved. The official investigation identified bolt-group shear failure and separation at a tower-top distribution beam splice as the initiating physical event following second-stage tensioning of the No. 4 tie cable. These accident-specific findings are not redetermined in the present study; its original contribution is the evidence-status separation and engineering synthesis of the reported evidence through construction-stage load-path reconstruction, connection mechanics assessment, quantitative consistency checks, robustness interpretation, and safety barrier analysis. The reported bolt deficiencies are examined through a limited author-derived normalization combining the material-strength and threaded shear-plane effects at the individual-fastener level; this indicator is used only as a consistency check and is not interpreted as bolt-group or complete splice capacity. Enlarged and irregular holes, missing fasteners, and unauthorized field modifications are interpreted as further impairing bolt-group load sharing. The evidence-constrained load-path assessment indicates that the officially identified splice separation would have interrupted a primary temporary support path while the permanent arch action had not yet developed. Visible cantilever descent was followed by cable rupture, arch-rib fracture, and extensive collapse within approximately 10 s. A qualitative fault-tree and bow-tie analysis organizes the reported procurement, fabrication, design, installation, inspection, monitoring, and governance deficiencies and identifies high-leverage preventive, verification, and consequence-limiting barrier pathways. The findings support consequence-based classification, independent verification, formal hold points, robustness assessment, and personnel exclusion for safety-critical temporary works. Full article

(This article belongs to the Special Issue Advances in Bridge Engineering: Structures, Monitoring, and AI Technologies)

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22 pages, 2468 KB

Open AccessArticle

A Cost-Effective Template-Matching Vision System for Non-Contact Displacement Monitoring: Laboratory Validation Against Linear Variable Displacement Transducers

by Valentina Picciano and Giuseppe Santarsiero

Infrastructures 2026, 11(9), 310; https://doi.org/10.3390/infrastructures11090310 - 1 Sep 2026

Abstract

Structural health monitoring increasingly relies on non-contact optical techniques to overcome the limitations of contact sensors such as linear variable displacement transducers (LVDTs) and accelerometers. This paper presents the development and assessment of a vision-based displacement measurement platform, built in Python with OpenCV, [...] Read more.

Structural health monitoring increasingly relies on non-contact optical techniques to overcome the limitations of contact sensors such as linear variable displacement transducers (LVDTs) and accelerometers. This paper presents the development and assessment of a vision-based displacement measurement platform, built in Python with OpenCV, implementing the Template Matching Method (TMM) for automatic tracking of user-defined regions of interest (ROIs) in video sequences. The platform was validated in a controlled laboratory environment through two configurations: a small circular target of 81 mm diameter and a much larger ROI on the flanges of an HEB 300 steel section, both instrumented with a reference LVDT. Video was acquired with a digital camera positioned frontally at 240 cm from the monitored elements, at 59.94 fps. Displacement time histories from template matching were compared against synchronous LVDT recordings using the coefficient of determination (R²), mean absolute error (MAE), root mean square error (RMSE) and mean percentage residual. The small ROI yielded R² = 0.9975, MAE = 0.89 mm and RMSE = 1.06 mm, while the larger ROI yielded R² = 0.9932, MAE = 1.22 mm and RMSE = 1.51 mm. Both approaches required decontamination of the rigid-body motion of the supporting plate to which the LVDT was attached. Near-millimetre accuracy was obtained on both a marker-dominated and a texture-dominated ROI, at a fraction of the cost of alternative approaches reported in the literature. Beyond the present validation, the platform is offered as a self-contained, reusable research tool for displacement monitoring in other experimental configurations. Full article

(This article belongs to the Special Issue Advances in Bridge Engineering: Structures, Monitoring, and AI Technologies)

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13 pages, 1772 KB

Open AccessArticle

Safety Effects of an Improved Highway Tunnel Lighting Environment: A Real-Vehicle Study of Drivers’ Visual and Physiological Responses

by Honglin Mu, Zhangwen Huang, Xinyuan Wang, Junshan Tian and Yanqun Yang

Infrastructures 2026, 11(9), 309; https://doi.org/10.3390/infrastructures11090309 - 1 Sep 2026

Abstract

Abrupt changes in the lighting environment at highway tunnel entrances, transition zones, and exits can impose substantial visual adaptation demands on drivers. This real-vehicle study evaluated a modified LED tunnel lighting environment designed to enlarge the effective luminous area, improve road surface lighting [...] Read more.

Abrupt changes in the lighting environment at highway tunnel entrances, transition zones, and exits can impose substantial visual adaptation demands on drivers. This real-vehicle study evaluated a modified LED tunnel lighting environment designed to enlarge the effective luminous area, improve road surface lighting uniformity, and reduce direct glare, compared with the original lighting system. The field experiment was conducted using 24 licensed drivers in a 610 m highway tunnel. Pupil area, mean fixation duration, and heart rate growth rate (HRG) were recorded in six longitudinal zones under the original and modified lighting conditions. The measured interior zone illuminance uniformity increased from 0.71 to 0.87 after modification. Repeated-measures ANOVA showed significant lighting-by-zone interactions for all three outcomes (p ≤ 0.001). Bonferroni-adjusted comparisons localized significant reductions in pupil area and HRG in the threshold, transition, and interior zones, while fixation duration increased significantly in those zones and in the exit zone. Across the averages for the six zones, pupil area decreased by 8.39%, HRG decreased by 13.78%, and mean fixation duration increased by 6.63%. The findings suggest that the modified lighting environment reduced visual adaptation demand and physiological arousal, especially in the threshold and transition zones. Fixation duration changes are interpreted as altered visual information processing rather than direct evidence of improved safety, and no inference about crash reduction can be made without direct driving performance or safety outcome data. Full article

(This article belongs to the Special Issue Advances in Road Infrastructure Safety)

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21 pages, 9156 KB

Open AccessArticle

A Lightweight Real-Time Pavement Distress Detection Network with Multi-Scale Coordinate Attention and Multi-Granularity Knowledge Distillation

by Dongpo Chen, Jiaxing Zou, Taibo Fan, Xinghua Wang, Zhong Dai, Wenjun Xing, Hao Feng, Zelin Qin and Xu Yang

Infrastructures 2026, 11(9), 308; https://doi.org/10.3390/infrastructures11090308 - 31 Aug 2026

Abstract

Automated pavement distress detection is essential for transportation infrastructure maintenance and road asset management. In practice, however, such detectors often need to run on embedded devices mounted on inspection vehicles, and two challenges hinder real-world deployment: (1) cracks and potholes possess markedly different [...] Read more.

Automated pavement distress detection is essential for transportation infrastructure maintenance and road asset management. In practice, however, such detectors often need to run on embedded devices mounted on inspection vehicles, and two challenges hinder real-world deployment: (1) cracks and potholes possess markedly different geometric priors—thin, elongated topology versus blob-like shapes—so a generic backbone tends to under-represent at least one class, and (2) accuracy-oriented detectors are typically too heavy for the embedded GPUs commonly mounted on inspection platforms. To address these issues, this paper proposes a lightweight real-time pavement distress detection network. First, a multi-scale coordinate attention (MSCA) module is embedded in the neck so that long-range row/column-wise dependencies are encoded together with multi-scale local context, which is helpful for slender cracks while remaining computationally efficient. Second, a slender-aware detection head (SADH) couples a 1 × k/k × 1 asymmetric branch with the standard square branch, giving the head an explicit inductive bias for elongated objects. Third, a multi-granularity knowledge distillation (MGKD) scheme is designed, which transfers teacher knowledge from a heavier teacher to the proposed student at three complementary granularities—pixel-level attention-masked features, instance-pair relations, and decoupled class-prior logits—thereby covering the three distinct levels of information that a multi-class dense detector relies on. The network is trained and evaluated on the public RDD2022 benchmark together with a supplementary in-house set of asphalt potholes. Under the fixed-seed, single-run evaluation used in this study, the proposed method achieves an mAP@0.5 of 71.65% on the author-defined test split, which is not directly comparable with evaluations on the official RDD2022 test set, with the comparison restricted to seven representative baselines evaluated under the same protocol, and runs at 72.5 FPS on an NVIDIA Jetson Orin Nano. Although its latency is modestly higher than that of YOLOv8s, it retains real-time inference capability for on-vehicle pavement inspection. Full article

(This article belongs to the Special Issue Pavement Performance and Maintenance: Smart Technologies and Sustainable Practices)

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24 pages, 21460 KB

Open AccessArticle

Transient Aerodynamic Loads and Structural Response of Fully Enclosed Noise Barriers Induced by High-Speed Trains

by Yan Bai, Wenfan Wang, Mingrui Zhang and Lu Guo

Infrastructures 2026, 11(9), 307; https://doi.org/10.3390/infrastructures11090307 - 31 Aug 2026

Abstract

Fully enclosed noise barriers (FENBs) are widely used in high-speed railway systems to mitigate environmental noise; however, the transient aerodynamic loads generated by train passage can induce complex structural responses. The relationship between the spatial–temporal evolution of these aerodynamic loads and the dynamic [...] Read more.

Fully enclosed noise barriers (FENBs) are widely used in high-speed railway systems to mitigate environmental noise; however, the transient aerodynamic loads generated by train passage can induce complex structural responses. The relationship between the spatial–temporal evolution of these aerodynamic loads and the dynamic response of the complete FENB structural system remains insufficiently understood. To address this issue, this study develops a sequential computational fluid dynamics–finite element analysis (CFD–FEA) framework that directly relates the transient pressure evolution during the complete train-passage process to the deformation and stress responses of the principal FENB components. The unsteady aerodynamic field generated by high-speed train passage is simulated using a moving-mesh CFD model, and the resulting time-dependent pressure loads are subsequently applied to a finite-element structural model. Train speeds ranging from 250 to 330 km/h are considered. The results reveal strongly transient and spatially non-uniform pressure distributions inside the FENB, characterized by nose-induced compression, a middle negative-pressure region, and wake-induced pressure fluctuations. Both structural deformation and equivalent stress increase with train speed, and the exit stage produces the most pronounced structural response because of the strong negative-pressure effect. Different structural components exhibit distinct response characteristics, with localized stress concentrations occurring in the glass panels and H-section steel columns. By establishing the correspondence between transient aerodynamic pressure evolution, train-passage stages, and component-level structural responses, this study provides a more comprehensive understanding of the aerodynamic load–structural response mechanism of FENBs and provides a basis for structural design and engineering assessment under increasing train speeds. Full article

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27 pages, 4793 KB

Open AccessArticle

Live Load Distribution Factors in Horizontally Curved Composite Steel I-Girder Bridges: FEM Assessment of AASHTO LRFD Provisions Under HL-93 and Iraqi HB115 Military Loading

by Oday Mohammed Albuthbahak

Infrastructures 2026, 11(9), 306; https://doi.org/10.3390/infrastructures11090306 - 30 Aug 2026

Abstract

The American Association of State Highway and Transportation Officials (AASHTO) Load and Resistance Factor Design (LRFD) live-load distribution-factor (DF) equations were calibrated on straight bridges, while their use for horizontally curved I-girder bridges is bounded by the L_as/R < 0.06 [...] Read more.

The American Association of State Highway and Transportation Officials (AASHTO) Load and Resistance Factor Design (LRFD) live-load distribution-factor (DF) equations were calibrated on straight bridges, while their use for horizontally curved I-girder bridges is bounded by the L_as/R < 0.06 rad criterion in Article 4.6.1.2.4b of the AASHTO LRFD Bridge Design Specifications, 10th ed. (2024). This study quantifies their accuracy beyond that limit using the finite element method (FEM) in 35 three-dimensional CSiBridge models subjected to numerical consistency checks: three composite plate-girder arrangements (4–6 girders, 9.0 m deck) at central angles of 0–15°, with near-limit, span-transfer, sensitivity, and out-of-range extensions to 25°, under the AASHTO LRFD vehicular design live-load model (HL-93) and the Iraqi Class 100 wheeled military vehicle (HB115; 1150 kN). At the limit, curvature amplification is only 1.8–2.6%. Beyond it, the exterior-moment equations become unconservative almost immediately; FEM demand exceeds AASHTO by 21–29% at 15°, whereas the interior-shear equations remain conservative. A two-part correction factor (CF) of the form CF = R₀[1 + (a + a₁S/L)(L/R)] is proposed (R² ≈ 0.97) and predicts the withheld out-of-range cases within 3.3%. Within the tested envelope, exterior-girder amplification depends primarily on L/R; for HB115, its rate is about half that of HL-93. Direct CSiBridge reconstruction of two published 1/10-scale laboratory specimens shows good agreement in global deflection and moderate agreement in strain-based transverse distribution. Because full-scale measurements for the exact 38 m reference configuration were unavailable, this evidence is treated as external experimental benchmarking of the modeling methodology rather than complete validation of the full parametric matrix. Full article

(This article belongs to the Special Issue Advances in Bridge Engineering: Structures, Monitoring, and AI Technologies)

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26 pages, 4029 KB

Open AccessReview

Performance Tailoring and Environmental Implications of Biochar-Modified Asphalt Materials: Toward Sustainable Road Design

by Yihui Ke, Enqi Pang, Williamson Gustave, Bi Gu, Hanbo Chen, Yumeng Song, Wei Lin, Xiaokai Zhang and Feng He

Infrastructures 2026, 11(9), 305; https://doi.org/10.3390/infrastructures11090305 - 28 Aug 2026

Abstract

Biochar is no longer considered merely a substitute for conventional fillers in asphalt materials; rather, it represents a multifunctional modifier that aligns with the goals of sustainable road design and urban mobility in smart cities. Its application now extends to the rheological modification [...] Read more.

Biochar is no longer considered merely a substitute for conventional fillers in asphalt materials; rather, it represents a multifunctional modifier that aligns with the goals of sustainable road design and urban mobility in smart cities. Its application now extends to the rheological modification of asphalt binders, mitigation of asphalt fume emissions, improvement in aging resistance and interfacial adhesion, and assessment of carbon sequestration potential. Biochar can improve the high-temperature stability, rutting and aging resistance, and asphalt–aggregate adhesion of asphalt materials in a suitable dosage, and at the same time reduce emissions of volatile organic compounds (VOCs), polycyclic aromatic hydrocarbons (PAHs), hydrogen sulfide (H₂S), and other fumes. However, the above effects are highly dependent on the biochar feedstock, production process, physicochemical properties, particle size, dosage and degree of dispersion. An excess amount or uneven distribution will reduce the crack resistance and fatigue life at low temperatures; phase separation may also occur and VOC emissions will increase. Therefore, the main problem in this area has shifted from whether biochar is effective to when it can be applied for particular pavement performance goals, what pollutant control targets are aimed for, and over what life-cycle periods. This review integrates evidence obtained at the binder, mastic, and mixture scales and critically evaluates the influence of biochar on pavement performance, fume emissions, aging, interfacial adhesion, and environmental safety. It also argues that empirical dosage selection should be replaced by coordinated optimization of biochar structure, material performance, emission mitigation, and life-cycle impacts. Verification of the low-carbon benefits and environmental safety of biochar-modified asphalt will ultimately require standardized assessment frameworks and consistently defined system boundaries. Ultimately, this work provides a foundation for integrating biochar-modified asphalt into eco-friendly and resilient road infrastructures, aligning with the goals of smart urban mobility and sustainable transportation. Full article

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28 pages, 24254 KB

Open AccessArticle

Punching Shear Behavior of Engineered Cementitious Composites Flat-Plate Slabs Incorporating Cement Kiln Dust and Crumb Rubber

by Rabie A. M. Amnisi, Mohamed E. El-Zoughiby, Basem S. Abdelwahed and Osama Youssf

Infrastructures 2026, 11(9), 304; https://doi.org/10.3390/infrastructures11090304 - 28 Aug 2026

Abstract

This study experimentally investigated the punching shear behavior of engineered cementitious composite flat-plate slabs incorporating cement kiln dust and crumb rubber. The considered criteria included replacing 50% of the rubber without treatment and treating the rubber at the same percentage; the flexural reinforcement [...] Read more.

This study experimentally investigated the punching shear behavior of engineered cementitious composite flat-plate slabs incorporating cement kiln dust and crumb rubber. The considered criteria included replacing 50% of the rubber without treatment and treating the rubber at the same percentage; the flexural reinforcement ratio, whether in the bottom tensile reinforcement ratio or in the top compressive reinforcement; and the ECC cube compressive strength (f_cu). For this purpose, thirteen reinforced flat-plate slabs were cast and tested. All slabs had the same dimensions of 1100 × 1100 × 100 mm, with a central square column that had dimensions equal to 160 × 160 × 160 mm. The flexural RFT ratios in the tension and compression zones were 1.0, 1.2, and 1.6%. The tested slabs were cast with different values of f_cu of 50, 65, and 70 MPa. The study first presented and discussed the first cracking load, ultimate load, crack pattern, load–deflection response, stiffness, and RFT strain. The experimental results demonstrated that increasing the tensile reinforcement RFT ratio significantly improved punching shear capacity by up to 33%, while the concrete cube compressive strength only contributed an approximately 14.2% increase. Treated crumb rubber engineered cementitious composite slabs showed greater initial stiffness and reduced deflections under the same loads, along with higher post-cracking stiffness degradation compared to crumb rubber concrete engineered cementitious composite slabs. Increased tension reinforcement improved initial and post-cracking stiffness and reduced deflections, with more significant effects in crumb rubber concrete engineered cementitious composite slabs. The flexural tension had a more substantial impact on punching shear behavior than compression. Comparisons with building design codes (ECP 203-2020, ACI 318-25, and Eurocode 2) revealed that while these codes could estimate shear capacity, they were conservative, with Eurocode 2 providing the best predictions by considering flexural tension. Full article

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23 pages, 5968 KB

Open AccessArticle

Application of Artificial Neural Networks in Modeling Drivers’ Comprehension of Road Markings

by Firas H. Asad, Raid R. A. Almuhanna, Ahmed K. Saeed and Karzan Ismael

Infrastructures 2026, 11(9), 303; https://doi.org/10.3390/infrastructures11090303 - 28 Aug 2026

Abstract

Road markings play a vital role in traffic safety and flow, yet their effectiveness relies entirely on drivers’ comprehension. This study seeks to assess the comprehension levels of a sample of drivers from Al-Najaf city (Iraq) and examine the extent to which their [...] Read more.

Road markings play a vital role in traffic safety and flow, yet their effectiveness relies entirely on drivers’ comprehension. This study seeks to assess the comprehension levels of a sample of drivers from Al-Najaf city (Iraq) and examine the extent to which their personal characteristics could influence these levels. While conventional linear statistical methods have provided foundational measures of driver comprehension, this study extends current research by utilizing a multilayer perceptron (MLP) artificial neural network (ANN) framework to capture complex, non-linear relationships between driver attributes and road marking comprehension in Al-Najaf, Iraq. Direct interviews were conducted with 402 drivers using a structured questionnaire to collect data on their personal attributes, driving behavior, and knowledge of 14 road markings. A set of correlational and group-comparison statistical analyses was initially performed before conducting the backpropagation-based ANN analysis; a supplemental sensitivity analysis for the best combination of activation functions and training/testing split ratios was performed. The analyses revealed an overall comprehension level of 72%. Crucially, the optimized ANN model revealed non-linear predictor importance hierarchies, demonstrating that drivers’ marking recognition and educational attainment are the primary determinants of conceptual comprehension, outweighing raw driving experience. These findings indicate that years of driving do not ensure adequate knowledge of road markings, revealing important limitations in current licensing standards. Consequently, this research offers an empirical foundation for transport authorities to transition from static licensing exams to continuous, adaptive driver education schemes targeting high-risk demographic groups. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Transportation Infrastructure: Intelligent Perception, Diagnosis, Prediction, and Decision-Making)

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21 pages, 3162 KB

Open AccessArticle

Deep Reinforcement Learning-Based Joint Control for Rotatable-Array UAV Transportation Communications

by Chen Zhang and Yi Xiong

Infrastructures 2026, 11(9), 302; https://doi.org/10.3390/infrastructures11090302 - 28 Aug 2026

Abstract

Future transportation networks may require aerial communication platforms capable of providing flexible and reliable services to vehicular terminals. In conventional unmanned aerial vehicle (UAV) communication systems, the antenna geometry is commonly treated as fixed, which limits the attainable directional gain when the relative [...] Read more.

Future transportation networks may require aerial communication platforms capable of providing flexible and reliable services to vehicular terminals. In conventional unmanned aerial vehicle (UAV) communication systems, the antenna geometry is commonly treated as fixed, which limits the attainable directional gain when the relative geometry between the UAV and users changes significantly. This work considered a UAV equipped with a mechanically reconfigurable antenna array and studied its joint motion and transmission control under finite-blocklength communication. A sequential optimization problem was formulated to maximize the accumulated user throughput by jointly optimizing the UAV trajectory, the array orientations, and the transmit beamforming vectors, subject to the UAV kinematic constraints, the UPA orientation constraints, and the transmission energy budget. The resulting problem involves nonlinear coupling among platform motion, antenna pointing, beamforming, and finite-blocklength rate expressions, making conventional optimization computationally demanding. To obtain an adaptive control policy, a soft actor–critic-based deep reinforcement learning method was developed. The simulation results showed that jointly controlling the UAV mobility, array orientation, and beamforming improves the achievable finite-blocklength transmission performance compared with benchmark schemes, demonstrating the effectiveness of the proposed framework in enhancing reliable data delivery for UAV-assisted transportation infrastructure applications. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Transportation Infrastructure: Intelligent Perception, Diagnosis, Prediction, and Decision-Making)

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24 pages, 12985 KB

Open AccessArticle

Technical, Economic, and Environmental Trade-Offs in Pavements with Lime-Stabilized Soils: A Sustainability-Oriented Approach

by Caroline Castilhos Rezende, Mônica Regina Garcez, Thaís Radünz Kleinert and Washington Peres Núñez

Infrastructures 2026, 11(9), 301; https://doi.org/10.3390/infrastructures11090301 - 28 Aug 2026

Abstract

This paper proposes a sustainability-oriented approach to evaluate the incorporation of lime-stabilized soils into pavement systems as a strategy to reduce the environmental impacts associated with conventional unbound granular structures. Pavement sections were designed for three tropical subgrades (Argisol, Latosol, and Luvisol), considering [...] Read more.

This paper proposes a sustainability-oriented approach to evaluate the incorporation of lime-stabilized soils into pavement systems as a strategy to reduce the environmental impacts associated with conventional unbound granular structures. Pavement sections were designed for three tropical subgrades (Argisol, Latosol, and Luvisol), considering five traffic levels ranging from low to high. Soil–lime layers containing 3% and 5% calcitic hydrated lime were compared with conventional unbound granular sections. Pavement design was performed through a mechanistic–empirical approach. Environmental impacts were quantified using life-cycle assessment from a cradle-to-construction perspective, including midpoint and endpoint indicators, and assessed together alongside relative construction costs. Pavement sections incorporating soil–lime layers reduced environmental impacts and relative costs for low and intermediate traffic levels, particularly for Argisol and Latosol subgrades. Reductions in global warming potential reached approximately 48%, while endpoint damage reductions exceeded 60% in some scenarios. However, the environmental benefits became progressively less pronounced as traffic levels increased. For the Luvisol subgrade, the lower suitability for lime stabilization required an additional graded crushed stone layer, reducing the environmental advantages of the stabilized systems. Soil–lime stabilization can represent an environmentally advantageous and economically competitive alternative for pavement design in tropical regions, particularly under low-to-moderate traffic conditions, although benefits may also be achieved at high traffic levels depending on subgrade characteristics. Full article

(This article belongs to the Special Issue Cold and Warm Techniques for Sustainable Pavement Construction and Maintenance)

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20 pages, 4589 KB

Open AccessArticle

Point-Cloud-Based 3D Inspection and Volume Quantification of Drainage-Pipeline Defects Using WCPC-GAN and Density-Adaptive Alpha Shapes

by Shuwei Zhai, Maolin Yao, Xingyi Wang, Lei Qiao and Niannian Wang

Infrastructures 2026, 11(9), 300; https://doi.org/10.3390/infrastructures11090300 - 28 Aug 2026

Abstract

Closed-circuit television (CCTV)-based inspection provides limited depth information and cannot directly quantify the three-dimensional geometry of drainage-pipeline defects. Moreover, the scarcity of annotated point-cloud data and the topological artifacts produced by conventional surface-reconstruction methods hinder automated condition assessment. This study presents a point-cloud-based [...] Read more.

Closed-circuit television (CCTV)-based inspection provides limited depth information and cannot directly quantify the three-dimensional geometry of drainage-pipeline defects. Moreover, the scarcity of annotated point-cloud data and the topological artifacts produced by conventional surface-reconstruction methods hinder automated condition assessment. This study presents a point-cloud-based framework for 3D inspection and volume quantification of concrete drainage-pipeline defects. WCPC-GAN expands the available defect data; PointNeXt segments the point clouds; and a RANSAC-constrained, density-adaptive Alpha Shape method reconstructs the defect surface for volume integration. The framework was evaluated using 15 independently fabricated circular, triangular, and rectangular defects, with reference volumes obtained through repeated water-displacement measurements. The proposed reconstruction achieved mean volume accuracies of 96.32 ± 0.67%, 96.78 ± 0.60%, and 96.86 ± 0.37%, respectively, and exceeded a contemporary CAP-UDF baseline by 1.62, 2.68, and 3.06 percentage points. Paired-bootstrap analysis estimated an overall error reduction of 5.56 percentage points over conventional Alpha Shape (95% confidence interval: 5.17–5.97). Synthetic augmentation also increased PointNeXt performance on the fixed real-only test set from 92.52% accuracy and 83.53% mIoU to 94.53% and 89.98%, respectively. The results support physically calibrated defect-volume assessment under controlled experimental conditions. Full article

(This article belongs to the Section Infrastructures Inspection and Maintenance)

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18 pages, 4583 KB

Open AccessArticle

BIM-Enabled Integration of Laboratory Quality Control Data for Railway Infrastructure Assets

by Francisco Andrade, João Ventura, Cristina Ribeiro, Rui Gavina, Ricardo Santos, Rosário Oliveira and Diogo Ribeiro

Infrastructures 2026, 11(9), 299; https://doi.org/10.3390/infrastructures11090299 - 26 Aug 2026

Abstract

Quality control data for construction materials is frequently exchanged as heterogeneous documents, limiting traceability and making element-level retrieval slow and error-prone. This study develops and assesses a digital methodology that integrates laboratory test results with BIM-referenced assets and delivers the integrated information via [...] Read more.

Quality control data for construction materials is frequently exchanged as heterogeneous documents, limiting traceability and making element-level retrieval slow and error-prone. This study develops and assesses a digital methodology that integrates laboratory test results with BIM-referenced assets and delivers the integrated information via interactive 3D-enabled dashboards. The methodology comprises three stages, starting with the acquisition of data from laboratory deliverables and 3D models, followed by data standardisation and relational structuring in the software Power BI Desktop (version 2.157.879.0, Microsoft Corporation, Redmond, WA, USA), and finally the publishing of generated dashboards embedded in a web application environment. The methodology is assessed through a real case study of a railway infrastructure asset, showing how laboratory records can be accessed and interpreted within a 3D model context, while preserving stakeholder-specific visibility through access control. The proposed approach supports element-level navigation of quality control and provides a practical pathway for laboratories to centralise, filter, and communicate test results without embedding full datasets into the BIM environment. Full article

(This article belongs to the Special Issue Building Information Modeling (BIM) for Civil Infrastructures)

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17 pages, 3889 KB

Open AccessArticle

Establishing an In-Situ Baseline Mechanical Monitoring Framework for Asphalt Pavements Using Embedded Strain Sensors

by Jon Zubizarreta-Azcuna, Rubén Machín-Ledesma, Pierre-Yves Clermont, Jon Ander Almandoz-Garmendia and Jose Luis Vilas-Vilela

Infrastructures 2026, 11(9), 298; https://doi.org/10.3390/infrastructures11090298 - 26 Aug 2026

Abstract

Asphalt pavements undergo progressive mechanical changes during service life due to traffic loading, temperature variations, moisture and material ageing. Embedded strain sensors can support in-situ pavement performance monitoring, but their response is strongly affected by experimental variables that must be identified before reliable [...] Read more.

Asphalt pavements undergo progressive mechanical changes during service life due to traffic loading, temperature variations, moisture and material ageing. Embedded strain sensors can support in-situ pavement performance monitoring, but their response is strongly affected by experimental variables that must be identified before reliable long-term ageing indicators can be established. This study establishes an in-situ baseline mechanical monitoring framework for asphalt pavements using embedded resistive strain transducers. KM-100HAS sensors were installed in an asphalt test section and evaluated through controlled field campaigns. A 17-point cross-pattern loading procedure was used to validate sensor location and orientation after construction. Load-free monitoring windows were analysed to estimate strain–temperature sensitivity and assess thermal correction of static loading–recovery tests. The results showed that loading position strongly conditions the measured strain response. Passive monitoring indicated that strain–temperature sensitivity depends on both temperature level and sensor location. In the mechanical tests, normalization of the recovery branch and logarithmic fitting over the first 200 s provided a consistent recovery-shape descriptor. The resulting slope,

b_{l o g 200}

, showed a strong linear relationship with the recovery percentage after 10 min (R² = 0.855). The proposed workflow provides a standardized baseline protocol for asphalt pavement monitoring and its mechanical evolution. Full article

(This article belongs to the Special Issue Pavement Performance and Maintenance: Smart Technologies and Sustainable Practices)

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24 pages, 40484 KB

Open AccessArticle

BC-GECO2: A Coarse and Fine Aggregate Segmentation and Counting Method for Hydraulic Concrete with Dense Depth Feature Fusion and Edge Enhancement

by Jiandong Wu, Baijing Wu, Jianwei Deng, Long Ma, Shuhong Liu and Shufan Zhang

Infrastructures 2026, 11(9), 297; https://doi.org/10.3390/infrastructures11090297 - 25 Aug 2026

Abstract

To reduce aggregate gradation counting errors caused by over-segmentation and under-segmentation of stacked and clustered aggregates with mixed types and diverse spatial distributions in hydraulic concrete, this study proposes BC-GECO2, a coarse and fine aggregate segmentation and counting method. Firstly, a BAHiera feature [...] Read more.

To reduce aggregate gradation counting errors caused by over-segmentation and under-segmentation of stacked and clustered aggregates with mixed types and diverse spatial distributions in hydraulic concrete, this study proposes BC-GECO2, a coarse and fine aggregate segmentation and counting method. Firstly, a BAHiera feature extraction network is designed to extract multi-scale deep features through edge-aware attention. In addition, a DFG-Edge module is developed to enhance the boundary features of densely distributed aggregates by integrating wavelet transform with a gated fusion mechanism, thereby alleviating the loss of small aggregate features during downsampling. Secondly, a CSFM-GFFCA module is constructed, in which a dual-branch structure is employed to adaptively fuse adjacent-scale features, strengthen the edge responses of densely distributed small aggregates, and enhance cross-layer feature interaction. Finally, a joint optimization function combining Focal loss and counting loss is established to guide the model toward hard-to-classify pixels, especially boundary pixels, thereby improving segmentation integrity and counting accuracy. Experiments conducted on an aggregate dataset collected from practical construction sites show that, compared with the baseline GECO2 model, the proposed method improves the average segmentation IoU, Dice, and BIoU by 2.92%, 5.04%, and 2.83%, respectively, while reducing the average counting MAE and RMSE by 6.92 and 15.65, respectively. Moreover, BC-GECO2 exhibits superior robustness and generalization capability under different stacking densities and blurred-boundary scenarios, providing technical support for the intelligent development of rapid concrete gradation detection. Full article

(This article belongs to the Section Infrastructures Materials and Constructions)

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21 pages, 3558 KB

Open AccessFeature PaperArticle

A Timed Petri Net Method to Optimize the Scheduling of a Railway Hub Construction Project

by Wei Wang and Enjian Yao

Infrastructures 2026, 11(9), 296; https://doi.org/10.3390/infrastructures11090296 - 25 Aug 2026

Abstract

The construction of large-scale buildings often faces extended production cycles due to inefficiencies in scheduling processes. To address this challenge, a timed Petri net model was developed to analyze and optimize construction scheduling. Based on the Petri net transition sequence, a scheduling optimization [...] Read more.

The construction of large-scale buildings often faces extended production cycles due to inefficiencies in scheduling processes. To address this challenge, a timed Petri net model was developed to analyze and optimize construction scheduling. Based on the Petri net transition sequence, a scheduling optimization model was proposed. To solve the model efficiently, an improved brainstorming optimization (BSO) algorithm was introduced. Compared with the classical BSO, two targeted enhancements were introduced: a problem-specific encoding and decoding method for Petri net transition sequences to ensure solution feasibility and an embedded simulated annealing local search mechanism to prevent premature convergence in later iterations. The proposed methodology was validated using real-world data from a large high-speed railway hub foundation pit construction project. Results demonstrated a significant reduction of 531 working hours in the total scheduling time, representing a 15.47% improvement in scheduling efficiency compared to traditional sequential scheduling methods. This approach not only shortened the scheduling cycle and enhanced production efficiency but also offered an innovative solution to address scheduling issues in complex construction processes. Full article

(This article belongs to the Special Issue High-Speed Railway Safety: Design, Development and Challenges)

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