Infrastructures

24 pages, 6008 KB

Open AccessArticle

Toward Sustainable Urban Mobility: A Multimodal Large Language Model (MLLM) Framework for Automated Driver Performance Assessment with YOLOv8-Based Scene Detection

by Mamatha Byreddy, Yara Zayed, Anas Alsobeh, Huthaifa I. Ashqar, Mohammed Elhenawy and Asmaa Alazmi

Infrastructures 2026, 11(9), 320; https://doi.org/10.3390/infrastructures11090320 - 8 Sep 2026

Abstract

Accurate and scalable driver performance assessment is critical for improving road safety and reducing traffic-related injuries and fatalities, particularly in low- and middle-income countries where the majority of global road deaths occur. This paper presents an exploratory proof-of-concept framework for automated driver evaluation [...] Read more.

Accurate and scalable driver performance assessment is critical for improving road safety and reducing traffic-related injuries and fatalities, particularly in low- and middle-income countries where the majority of global road deaths occur. This paper presents an exploratory proof-of-concept framework for automated driver evaluation that combines real-world dashcam footage, YOLOv8-based object detection, and multimodal large language models (MLLMs), specifically Gemini 1.5 Flash. Two prompting strategies, narrative and rule-based, were designed to assess driver behavior against standardized licensing criteria derived from the California Department of Motor Vehicles (DMV) driving performance evaluation score sheet. The framework was evaluated across 11 manually curated driving scenarios covering intersections, pedestrian crossings, stop signs, cyclists, and emergency vehicles. Ground-truth labels were established through consensus between two traffic engineering experts cross-referencing official California DMV evaluation criteria. In this preliminary evaluation, the rule-based prompt achieved higher agreement with ground-truth assessments (10/11 scenarios, 90.9%) compared to the narrative prompt (7/11 scenarios, 63.6%), particularly in detecting clear rule violations. The narrative approach demonstrated greater contextual flexibility in ambiguous situations. These results should be interpreted as preliminary, given the small sample size, manually curated dataset, and absence of large-scale statistical validation. Nonetheless, the findings illustrate how combining visual detection with structured language-model prompting may support interpretable, policy-aligned driver evaluation. Key limitations include dependence on video quality, limited scenario diversity, absence of temporal behavioral modeling, and reproducibility constraints tied to proprietary API behavior. Future work should expand validation to larger annotated datasets, incorporate temporal sequence modeling, and explore region-specific regulatory adaptation. Full article

(This article belongs to the Special Issue Sustainable Road Design and Traffic Management)

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20 pages, 4793 KB

Open AccessArticle

Analytical Solution for Tension Piles Supporting Large Civil Infrastructures in Three-Layered Soil

by Sudip Basack, Meshel Q. Alkahtani, Saiful Islam, Hadi Khabbaz and Moses Karakouzian

Infrastructures 2026, 11(9), 319; https://doi.org/10.3390/infrastructures11090319 - 8 Sep 2026

Abstract

Pile foundations transmit structural loads to deeper subsoil strata whenever the soil in the vicinity of the ground surface lacks sufficient strength and stiffness to ensure an adequate factor of safety against ultimate failure or warrant settlements to remain below acceptable limits. In [...] Read more.

Pile foundations transmit structural loads to deeper subsoil strata whenever the soil in the vicinity of the ground surface lacks sufficient strength and stiffness to ensure an adequate factor of safety against ultimate failure or warrant settlements to remain below acceptable limits. In many in situ conditions, piles are embedded in layered subsoil medium. In several circumstances, piles are subjected to tensile loading. Large and high-rise civil infrastructure subjected to wind loading, transport infrastructure under horizontal loading due to moving vehicles, offshore structures withstanding wind and wave loading, underground structures subjected to hydrostatic pressure due to buoyancy, etc., are some examples where tension loads are imparted on the supporting piles. The imparted uplift loads in these tension piles are balanced by the negative skin friction induced at the pile–soil interface. In this paper, an analytical model using systematic application of established upper bound shear stress theory to three-layered soil configurations has been developed to formulate the ultimate uplift capacity of tension piles in three-layered soil. The model adopted appropriate correlations for upper bound interface shear stresses in different soils as well as tensile failure of pile material itself. The developed solution was validated by comparing with available experimental results. Thereafter, a case study was performed to study the influence of the variation of pile geometries and relative stiffness on ultimate uplift capacities. Important conclusions were drawn from the entire study. Full article

(This article belongs to the Special Issue Recent Developments in Experimental, Analytical and Numerical Methods for Civil Infrastructure Projects)

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31 pages, 11305 KB

Open AccessArticle

A Comparative Study of Metallic Mild Steel Dampers and Fluid Viscous Dampers in Reinforced Concrete Structures Based on Nonlinear Time History Analysis

by Zhenwen Gong and Pengfei Ma

Infrastructures 2026, 11(9), 318; https://doi.org/10.3390/infrastructures11090318 - 8 Sep 2026

Abstract

Existing comparative studies on metallic mild steel dampers (SDs) and fluid viscous dampers (FVDs) are primarily limited by the coupling of device type with layout variations, the lack of a unified performance metric, and the absence of multi-level evidence under fixed structural configurations. [...] Read more.

Existing comparative studies on metallic mild steel dampers (SDs) and fluid viscous dampers (FVDs) are primarily limited by the coupling of device type with layout variations, the lack of a unified performance metric, and the absence of multi-level evidence under fixed structural configurations. This study overcomes these limitations by comparing SDs and FVDs under strictly identical conditions—same RC frame, same 26 damper locations, same ground motions, and a unified code-specified drift target—across frequent, design-basis, and rare earthquake levels, supplemented by energy dissipation and added damping ratio analyses. Under frequent earthquakes (FEs), the FVD achieves a maximum story-shear reduction of 33% and effectively controls inter-story drift through its velocity-dependent energy-dissipation mechanism. Under rare earthquakes (REs), the SD demonstrates superior performance, providing a 35% maximum story-shear reduction, while maintaining inter-story drift ratios within code-specified limits, owing to its combined stiffness and damping contributions. In terms of energy dissipation, the total cumulative energy dissipated by FVDs is 39.4–67.6% higher than that of SDs under the same ground motions, with added damping ratios averaging 2.42% for FVDs and 2.86% for SDs. These findings suggest that FVDs are more favorable for serviceability and frequent seismic performance, while SDs exhibit better response reduction effects under rare earthquake excitations. Full article

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27 pages, 18062 KB

Open AccessArticle

Seismic Response of Concrete Columns Reinforced with CFRP Bars and Spirals Under Near-Fault Ground Motions

by Minh Quang Vo and Takeshi Maki

Infrastructures 2026, 11(9), 317; https://doi.org/10.3390/infrastructures11090317 - 8 Sep 2026

Abstract

Carbon-fiber-reinforced polymer (CFRP) reinforcement is a potential alternative to steel in corrosive environments. However, CFRP is elastic without ductility, and the seismic performance of CFRP-reinforced concrete (RC) columns is inadequately understood. This study characterizes the intrinsic seismic response of concrete columns reinforced with [...] Read more.

Carbon-fiber-reinforced polymer (CFRP) reinforcement is a potential alternative to steel in corrosive environments. However, CFRP is elastic without ductility, and the seismic performance of CFRP-reinforced concrete (RC) columns is inadequately understood. This study characterizes the intrinsic seismic response of concrete columns reinforced with CFRP cable-type bars and spirals under recorded near-fault ground motions. Three reference steel-RC columns are designed as seismic-resistant, non-seismic-resistant, and with post-cracking stiffness equivalent to the CFRP-RC column. The CFRP-RC and seismic-resistant steel-RC columns were tested under cyclic loading, and the results validated finite element (FE) models. Validated models simulated four columns under cyclic loading, and under 11 near-fault records matched to a capacity-derived elastic target spectrum. The results, bounded by selected ground motions and material constitutive models, show that: (1) The tested CFRP-RC column dissipated about 50% less energy than the steel-RC reference; (2) No material-level failure criterion was met under the suite, although peak base shears exceeded the nominal quasi-static capacities; (3) The CFRP-RC column developed the largest transient drift but minimal residual drift, whereas the steel-RC columns limited transient amplitude via hysteretic dissipation yet accumulated permanent offsets; (4) Response of the CFRP-RC column depends on ground motion energy delivery characteristics: concentration, symmetry, and duration. Full article

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

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32 pages, 7185 KB

Open AccessArticle

Assessing the Impact of Roadside and Median Safety Barriers on Available Sight Distance on Croatian Motorways

by Ivica Stančerić, Igor Majstorović and Željko Stepan

Infrastructures 2026, 11(9), 316; https://doi.org/10.3390/infrastructures11090316 - 7 Sep 2026

Abstract

Available sight distance (ASD) on motorways represents the unobstructed line of sight from the driver’s perspective to an obstacle on the carriageway, which must equal or exceed the required stopping sight distance (SSD). This study evaluates ASD across both two-dimensional (2D) and three-dimensional [...] Read more.

Available sight distance (ASD) on motorways represents the unobstructed line of sight from the driver’s perspective to an obstacle on the carriageway, which must equal or exceed the required stopping sight distance (SSD). This study evaluates ASD across both two-dimensional (2D) and three-dimensional (3D) motorway alignments, encompassing right- and left-hand horizontal curves combined with different vertical alignments, as well as roadside and median barriers. 2D analyses were performed on predefined theoretical profiles, while 3D ASD evaluations were conducted on real-world models of existing motorway sections in Croatia. These sections were selected based on their diverse horizontal and vertical geometric alignments, design eras, traffic characteristics, and historical crash data regarding collisions with obstacles. The findings reveal that safety barrier placement on motorways can severely constrain sightlines, demonstrating that even radii exceeding the regulatory minimums are insufficient to guarantee necessary SSD requirements. Visibility restrictions from safety barriers are especially pronounced along barrier-restricted horizontal curves, primarily on right-hand curves and occasionally on left-hand curves, depending on the radius. In certain real-world sections, cut slopes also restrict the required visibility. Current Croatian guidelines rely on 2D analysis, which fails to account for safety barriers obstructing visibility. This research emphasises the critical necessity of conducting continuous 3D sight visibility simulations during initial design phases, while recommending further research that could be implemented to update regulatory frameworks regarding parameters for ASD analysis. Full article

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23 pages, 1751 KB

Open AccessArticle

An Enhanced CEB MC90 Model for Total Shrinkage Prediction in CNT-Reinforced Concrete with Monte Carlo-Based Probabilistic Assessment

by Masoumeh Khamehchi, Akram M. Mhaya, Iman Faridmehr and Ghasan Fahim Huseien

Infrastructures 2026, 11(9), 315; https://doi.org/10.3390/infrastructures11090315 - 7 Sep 2026

Abstract

Total shrinkage, encompassing both drying and autogenous shrinkage components under standard drying conditions, is one of the most important factors affecting the long-term durability and serviceability of concrete structures. However, accurately predicting shrinkage behavior in nanomodified concrete remains a significant challenge. This study [...] Read more.

Total shrinkage, encompassing both drying and autogenous shrinkage components under standard drying conditions, is one of the most important factors affecting the long-term durability and serviceability of concrete structures. However, accurately predicting shrinkage behavior in nanomodified concrete remains a significant challenge. This study proposes an improved predictive framework for estimating the total shrinkage strain of carbon nanotube (CNT)-reinforced concrete. The developed model extends the CEB MC90 shrinkage model by incorporating critical CNT-related parameters, including CNT content, aspect ratio, and type, together with the water-to-cement ratio. The proposed framework was validated using experimental results. Additionally, a Monte Carlo simulation comprising 100,000 stochastic realizations was performed to evaluate the influence of uncertainties in key input variables, namely curing time, water-to-cement (w/c) ratio, CNT content, CNT aspect ratio, and CNT type. The simulation quantifies the success probability, defined as the likelihood that the total shrinkage strain of CNT-reinforced concrete remains within acceptable design limits (i.e., achieving at least a 10% reduction compared to plain concrete). The results demonstrate that the enhanced model provides accurate predictions of total shrinkage, with overall prediction errors of approximately 2% for plain concrete and 5% for CNT-modified concrete. The findings also show that the incorporation of CNTs effectively reduces total shrinkage by refining the cementitious matrix and improving internal restraint within the composite. Consequently, the proposed probabilistic prediction model offers a practical and reliable tool for optimizing CNT-reinforced concrete mixtures, enabling shrinkage to remain within acceptable design limits while improving long-term dimensional stability and structural durability. Full article

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30 pages, 6792 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 - 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)

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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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