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Infrastructures
Volume 11
Issue 8
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Infrastructures, Volume 11, Issue 8 (August 2026) – 38 articles

Cover Story (view full-size image): This paper presents the development and validation of a highly circular asphalt surface mixture incorporating steel slag aggregates and reclaimed asphalt pavement (RAP). The optimized AC14 mixture included 81% secondary materials, or 83% when recovered filler was also considered, and was validated from laboratory design to plant production and field application. Compared with a conventional mixture, it showed improved rutting resistance, higher stiffness, very high moisture resistance, and favorable fatigue indicators. Life cycle assessment and cost analysis also showed lower environmental impacts and reduced production-stage costs, highlighting the potential of circular asphalt mixtures for more sustainable road infrastructure. View this paper
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28 pages, 15576 KB  
Article
Synthetic Data Generation for the Prototyping of Bridge Damage Detection Algorithms
by Matvei Sinden and Alejandro Jiménez Rios
Infrastructures 2026, 11(8), 293; https://doi.org/10.3390/infrastructures11080293 - 21 Aug 2026
Viewed by 220
Abstract
The application of Machine Learning (ML) to bridge Structural Health Monitoring (SHM) is constrained by a lack of diverse and labelled datasets. Obtaining high-quality training data from operational infrastructure is inherently difficult because critical assets are typically repaired immediately upon the detection of [...] Read more.
The application of Machine Learning (ML) to bridge Structural Health Monitoring (SHM) is constrained by a lack of diverse and labelled datasets. Obtaining high-quality training data from operational infrastructure is inherently difficult because critical assets are typically repaired immediately upon the detection of defects, preventing the collection of data describing diverse failure modes. To address this scarcity and enable the prototyping of robust algorithms, this study presents a framework for generating synthetic modal frequencies using a calibrated Finite Element (FE) model of the S101 bridge. Aleatory uncertainties and environmental variability are incorporated through the stochastic variation of material properties and thermal loads derived from a 20-year climate record. Analysis of the generated dataset revealed that simulated thermal loads induced frequency shifts that often exceeded those caused by minor structural damage, confirming the necessity of training on environmentally representative data. The primary contribution of this work is an open-access, FAIR-compliant (Findable, Accessible, Interoperable, Reusable) synthetic dataset, intended to serve as a standardised benchmark for the SHM research community under conditions of combined structural and environmental uncertainty. To demonstrate the utility of the generated data, the performance of a supervised multi-layer perceptron and an unsupervised k-means clustering algorithm are evaluated, with the supervised approach achieving a maximum classification accuracy of 1.00. However, the framework also reveals a fundamental modelling limitation: the linear FE approach failed to replicate the physical response under pier settlement, producing frequency shifts an order of magnitude below those observed experimentally. Full article
(This article belongs to the Special Issue Advances in Structural Health Monitoring and Industry 5.0 Innovations for Bridge Management and Conservation)
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18 pages, 4015 KB  
Article
Behavior of Sustainable Functionally Graded Beams Made of Hybrid Geopolymer Concrete
by Ahmed Al-Mowafy, Mohamed E. El-Zoughiby, El-Sayed Abd-Elaal, Mohamed Ghalla, Mohanad Abdulazeez and Osama Youssf
Infrastructures 2026, 11(8), 292; https://doi.org/10.3390/infrastructures11080292 - 21 Aug 2026
Viewed by 343
Abstract
Functionally graded concrete (FGC) is an advanced building technique that allows structural components to satisfy diverse mechanical and durability specifications. In this study, hybrid geopolymer concrete (HGC) was used in constructing reinforced FGC beams to improve their flexural performance and sustainability. One HGC [...] Read more.
Functionally graded concrete (FGC) is an advanced building technique that allows structural components to satisfy diverse mechanical and durability specifications. In this study, hybrid geopolymer concrete (HGC) was used in constructing reinforced FGC beams to improve their flexural performance and sustainability. One HGC mix was used and compared with the corresponding control cement-based concrete mixture. The HGC mix was made of FA, slag, and dolomite powder (DP) binders. The structural performance of ten reinforced FGC beams, with different cross-sections and varying concrete layer configurations, was evaluated by four-point bending tests. The results indicated that beams utilizing HGC in the tensile zone showed improved ductility and load-carrying capacity relative to those constructed with the corresponding cement-based concrete. The functionally graded configuration, particularly when HGC is placed in the crucial tension zone, markedly enhanced both the ultimate deflection by 30% and ductility by 73%. These findings highlighted the advantages of using HGC in constructing FGC beams for sustainable, high-performance concrete structures, facilitating diminished cement usage, reduced carbon emissions, and enhanced structural resilience. Full article
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26 pages, 9690 KB  
Article
Integrating Bayesian Inference into Structural Parameter Estimation: A Python-Based Approach Using OpenSeesPy and PyMC
by Oscar D. Hurtado, Felipe Guerrero, Albert R. Ortiz and Daniel Gomez
Infrastructures 2026, 11(8), 291; https://doi.org/10.3390/infrastructures11080291 - 20 Aug 2026
Viewed by 267
Abstract
In structural engineering, accurate prediction of structural behavior is crucial for ensuring safety and reliability. Traditional parameter estimation methods often rely on deterministic approaches, which may overlook inherent uncertainties in real-world structures. This paper presents a comprehensive manual on utilizing a Bayesian framework [...] Read more.
In structural engineering, accurate prediction of structural behavior is crucial for ensuring safety and reliability. Traditional parameter estimation methods often rely on deterministic approaches, which may overlook inherent uncertainties in real-world structures. This paper presents a comprehensive manual on utilizing a Bayesian framework to update structural model parameters, offering a robust strategy for quantifying uncertainties and enhancing predictive accuracy. The methodology employs Python, leveraging Open-SeesPy for finite element modeling and PyMC for probabilistic inference. Five distinct examples are provided to illustrate the workflow, ranging from fundamental parameter estimation in structural frames to advanced Hierarchical Stochastic Models (HSMs) for constitutive material calibration. This work serves as a practical guide for structural engineers seeking to adopt novel probabilistic techniques. By integrating Bayesian inference, engineers can effectively account for both measurement noise and intrinsic physical variability, thereby improving the fidelity of predictive models. The use of open-source tools streamlines the implementation process, making these advanced methods accessible to a wider audience in engineering practice. Full article
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15 pages, 2626 KB  
Article
Extrapolation of Site-Specific Live Load for Bridge Monitoring
by Aneta K. Luszczynska and Andrzej S. Nowak
Infrastructures 2026, 11(8), 290; https://doi.org/10.3390/infrastructures11080290 - 19 Aug 2026
Viewed by 240
Abstract
Currently in the United States, there are more than 623,000 bridges of which 6.8% are in poor condition. Over 100 million trips are taken across these structurally deficient bridges every day and the bridge-related system rehabilitation need is estimated at $191 billion. In [...] Read more.
Currently in the United States, there are more than 623,000 bridges of which 6.8% are in poor condition. Over 100 million trips are taken across these structurally deficient bridges every day and the bridge-related system rehabilitation need is estimated at $191 billion. In 1990’s, calibration of AASHTO LRFD Bridge Design Code involved extrapolation of distributions to evaluate the mean maximum 75-year live loads. However, this derivation was based on a survey of Ontario trucks with small sample size. In the meantime, the Weigh-in-Motion technology improved, and millions of vehicles are measured in various locations on a continuous basis. The objective of this study is to examine site-specific live load spectra based on WIM database in Alabama. Massive volume of data collected by the Department of Transportation is used to compute moments and shears for spans ranging from 30 ft (9 m) to 200 ft (60 m). Cumulative distribution functions of bias ratios (WIM Truck/HL-93 Loading) were plotted on normal probability paper and extrapolated. The findings show that AASHTO provisions for Strength I Limit State are not representative of vehicles at WIM sites located on interstate highways. The findings confirm the importance of continuous traffic data collection and use of reliability analysis procedures. Full article
(This article belongs to the Special Issue Advances in Structural Health Monitoring and Industry 5.0 Innovations for Bridge Management and Conservation)
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29 pages, 18253 KB  
Article
Research on a Hybrid Prediction and Early Warning Model for Concrete Bridge Settlement Based on VMD–TCN–BiLSTM
by Teng Zhao, Yue Zhai, Shangxue Lei, Shengyu Wei, Shaoxun Hao, Yunsheng Zhang and Ruili Zong
Infrastructures 2026, 11(8), 289; https://doi.org/10.3390/infrastructures11080289 - 13 Aug 2026
Viewed by 232
Abstract
Bridge settlement prediction is essential for understanding structural deformation evolution and supporting bridge condition assessment. However, settlement monitoring sequences usually exhibit nonlinear characteristics, non-stationarity, and multi-scale fluctuations, which increase the difficulty of accurately capturing deformation evolution trends. This study develops an integrated settlement [...] Read more.
Bridge settlement prediction is essential for understanding structural deformation evolution and supporting bridge condition assessment. However, settlement monitoring sequences usually exhibit nonlinear characteristics, non-stationarity, and multi-scale fluctuations, which increase the difficulty of accurately capturing deformation evolution trends. This study develops an integrated settlement prediction and dynamic early warning framework for concrete bridges by combining Variational Mode Decomposition (VMD), Temporal Convolutional Network (TCN), and Bidirectional Long Short-Term Memory Network (BiLSTM). In the proposed framework, VMD is employed to decompose settlement monitoring sequences into multiple components with different frequency characteristics, TCN is used to extract local temporal variation features, and BiLSTM is applied to capture long-term temporal dependencies for settlement prediction. Based on the predicted settlement responses, settlement increment, settlement rate, and differential settlement indicators are further incorporated to establish a dynamic early warning method for deformation risk identification. Continuous monitoring data from an in-service reinforced concrete bridge were used for validation. The results show that the proposed framework achieves better prediction performance than benchmark models, with an R2 of 0.956, MAE of 0.38 mm, RMSE of 0.51 mm, and MAPE of 1.52%. Furthermore, simulated abnormal settlement scenarios were constructed to evaluate the response capability of the warning framework under different deformation conditions. The results demonstrate that the proposed framework can effectively integrate settlement prediction and deformation risk assessment, providing a potential approach for bridge settlement monitoring and early warning analysis. Full article
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18 pages, 3079 KB  
Article
Analytical Method for Shear Stress in CSW Composite Girders Accounting for Cross-Beam Constraint Effects near Intermediate Supports
by Dashuai Wang, Wenyan Geng and Zhaohua Liu
Infrastructures 2026, 11(8), 288; https://doi.org/10.3390/infrastructures11080288 - 13 Aug 2026
Viewed by 259
Abstract
The shear behavior of corrugated steel web (CSW) composite girders near intermediate supports differs fundamentally from that of conventional beam segments due to the rigid restraint of concrete cross-beams, yet current design codes incorrectly assume the CSWs resist the entire shear force. This [...] Read more.
The shear behavior of corrugated steel web (CSW) composite girders near intermediate supports differs fundamentally from that of conventional beam segments due to the rigid restraint of concrete cross-beams, yet current design codes incorrectly assume the CSWs resist the entire shear force. This paper presents a refined analytical method, based on a three-beam composite model, that explicitly accounts for the cross-beam constraint effect. By assuming a quadratic parabolic distribution of the additional shear-flow intensity along the constraint zone, a closed-form expression for the effective shear force carried by the CSWs is derived. The proposed method is validated against three-dimensional finite element (FE) simulations and existing experimental data, and further corroborated by a parametric study covering varying structural configurations. The results confirm a shear redistribution mechanism characterized by “CSWs unloading and flange sharing.” At the section nearest to the cross-beam, the CSWs actually carry only 65.41% of the total shear force, while the flanges share approximately 35%. In contrast, the conventional code method, which neglects flange shear contribution, severely overestimates CSWs’ shear stress, producing an error as high as 35.29% at the section adjacent to the cross-beam. These findings demonstrate that the cross-beam constraint must be considered in shear design, especially near supports, and the proposed method offers a rational and accurate alternative to existing code provisions. Full article
(This article belongs to the Special Issue Advances in Bridge Engineering: Structures, Monitoring, and AI Technologies)
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20 pages, 2432 KB  
Article
Three-Dimensional Seepage Characteristics and Seepage-Control Performance of the Earth–Rockfill–Concrete Dam Connection at HS Reservoir
by Xinqi Zhao, Fengming Zhou, Yu Li, Yaohong Yang, Jialin Chen, Xiaoyuan Shen and Shoukai Chen
Infrastructures 2026, 11(8), 287; https://doi.org/10.3390/infrastructures11080287 - 12 Aug 2026
Viewed by 297
Abstract
Connections between earth–rockfill and concrete dams are critical components of hybrid-dam seepage-control systems because material-stiffness contrasts and complex foundation conditions can create localized preferential seepage paths. Using HS Reservoir as a case study, this predictive design-stage assessment employed a full-domain three-dimensional model of [...] Read more.
Connections between earth–rockfill and concrete dams are critical components of hybrid-dam seepage-control systems because material-stiffness contrasts and complex foundation conditions can create localized preferential seepage paths. Using HS Reservoir as a case study, this predictive design-stage assessment employed a full-domain three-dimensional model of the dam–foundation–abutment system and a local three-dimensional model of the cutoff-spur-wall connection. The seepage field, hydraulic gradients, and zonal seepage discharges were evaluated under the normal pool, design flood, and check flood levels, together with the responses of the connection interface and right-abutment grout curtain. Across the three baseline scenarios, the impervious core accounted for 82.2–83.6% of the total head difference at the maximum riverbed section, and the reported control-location gradients remained below the corresponding design values. At the check flood level, the modeled 178 and 179 m head contours passed above the local curtain crest at elevation 177.5 m, identifying an over-curtain seepage pathway. From the design flood level to the check flood level, right-abutment discharge increased from 259.86 to 544.49 m3/d (109.5%), while total discharge increased by 28.6%. Flow in the connection zone diverted around and beneath the cutoff spur wall, and the connection-surface gradients increased with reservoir level. These model predictions characterize the three-dimensional seepage response of the connection zone and right-abutment seepage-control system and can inform curtain-crest review, construction quality control, and post-impoundment monitoring. Full article
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30 pages, 1923 KB  
Article
Value-of-Information-Based Material Testing for Existing Reinforced Concrete Members
by Vittorio Palma, Simone Celati, Agnese Natali, Walter Salvatore and Sebastian Thöns
Infrastructures 2026, 11(8), 286; https://doi.org/10.3390/infrastructures11080286 - 11 Aug 2026
Viewed by 212
Abstract
This paper presents a predicted information and predicted action (PIPA) decision-analysis approach with Bayesian material updating for planning material testing in existing reinforced concrete members. The objective is to select the number of concrete and reinforcing-steel tests before testing is performed and before [...] Read more.
This paper presents a predicted information and predicted action (PIPA) decision-analysis approach with Bayesian material updating for planning material testing in existing reinforced concrete members. The objective is to select the number of concrete and reinforcing-steel tests before testing is performed and before a management action is chosen, accounting jointly for the updated structural performance, information-acquisition costs, action costs, and expected failure consequences. Predicted future test outcomes are used to update the material-strength distributions; the updated distributions are then propagated through the shear, flexural, and system reliability analyses to inform outcome-dependent action selection. Management interventions are modelled as system-state actions through action-dependent system failure probabilities. The optimal testing option is identified by minimising a total predicted-information and predicted-action cost-and-risk measure that combines information and expected action costs with the expected consequences of the system states. The approach is applied to a benchmark reinforced-concrete member with transverse shear reinforcement and uncertain concrete compressive strength and reinforcing-steel yield strength, in which the same steel-strength population is adopted for the longitudinal and transverse reinforcement. The decision-optimal testing option consists of six concrete tests and three reinforcing-steel tests, reducing the total expected decision cost-and-risk measure by approximately 58.2% relative to the no-new-information decision. The decision value arises mainly from avoiding unnecessary intervention when favourable material information is acquired. The results formulate material-test planning as a decision-value problem, providing an alternative to fixed sample-size rules while retaining explicit dependence on structural, probabilistic, action, and cost assumptions. Full article
(This article belongs to the Section Infrastructures and Structural Engineering)
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31 pages, 19203 KB  
Article
Interlayer Shear Response of Asphalt Bridge Deck Pavements Under Thermo-Mechanical Coupling and Moving Braking Loads
by Xuan Zhu, Zhi Li, Xiangyu Lei, Hailin Wang, Weiwei Lu, Dingling Yang, Hongyu Ren, Yuxi He, Weiguo Wu and Peng Chen
Infrastructures 2026, 11(8), 285; https://doi.org/10.3390/infrastructures11080285 - 10 Aug 2026
Viewed by 243
Abstract
Asphalt bridge deck pavements are highly susceptible to rutting, shoving, and interlayer slippage under high-temperature traffic conditions, where interlayer shear stress plays a decisive role. To clarify the coupled effects of thermal gradients and moving loads, this study developed a sequential three-dimensional thermo-mechanical [...] Read more.
Asphalt bridge deck pavements are highly susceptible to rutting, shoving, and interlayer slippage under high-temperature traffic conditions, where interlayer shear stress plays a decisive role. To clarify the coupled effects of thermal gradients and moving loads, this study developed a sequential three-dimensional thermo-mechanical finite element model for a double-layer pavement in Zhongshan, China. Field-recorded air temperature, solar radiation, sunshine duration, and wind speed were used to define transient thermal boundaries. The calculated temperature field was then transferred to a fully bonded moving-load model with dual rectangular contact areas and braking-induced longitudinal traction. Axle load, roadway slope, braking coefficient, and the thicknesses of the SMA-13 and AC-20 layers were varied. The predicted temperature fluctuation attenuated and the peak time was delayed with depth. The pavement surface reached 58.95 °C at 13:00, whereas the bottom of the asphalt overlay reached 46.99 °C at 17:00. Under the adopted 14:00 near-peak summer condition, increasing axle load amplified the overall response and raised the maximum asphalt-layer shear response from 0.172 to 0.223 Mpa. Roadway slope mainly affected traffic-direction stress transfer. Increasing the braking coefficient from 0 to 0.7 increased longitudinal shear response from 57.9 to 161.2 kPa in the asphalt layers and from 56.4 to 112.6 kPa near the AC-20/concrete interface. Increasing SMA-13 thickness reduced thermal and mechanical demand in the underlying layers, whereas increasing AC-20 thickness reduced the response near the concrete deck but shifted part of the tensile and shear demand toward the upper asphalt layer. Full article
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21 pages, 1766 KB  
Article
In Situ Assessment of Track Geometry Degradation and Defect Scoring for Maintenance Prioritization: A Case Study on a Railway Section Line M300, Romania
by Madalina Ciotlaus, Domuta Marcian, Alexandra Denisa Danciu, Vladimir Marusceac, Mihai Liviu Dragomir and Gabriela Pau
Infrastructures 2026, 11(8), 284; https://doi.org/10.3390/infrastructures11080284 - 10 Aug 2026
Viewed by 278
Abstract
Railway track geometry degradation is a key determinant of infrastructure safety, ride comfort, maintenance demand, and lifecycle cost. This paper presents a measurement-based assessment of track geometry degradation on the analyzed section of railway line M300 in Romania, with emphasis on its practical [...] Read more.
Railway track geometry degradation is a key determinant of infrastructure safety, ride comfort, maintenance demand, and lifecycle cost. This paper presents a measurement-based assessment of track geometry degradation on the analyzed section of railway line M300 in Romania, with emphasis on its practical use for maintenance prioritization. The study uses in situ track-geometry inspection records collected by a track-measuring vehicle (VMC) between 2020 and 2023 on a curved double-track sector, and evaluates the main geometric defect families used in railway practice, including alignment, gauge, longitudinal level, cross-level, and twist. In addition to conventional defect identification, severity grading, and penalty-point scoring, the paper uses a maintenance-oriented interpretation framework based on the campaign-level Defect Severity Index (DSI), the Defect Recurrence Factor (DRF), and the Maintenance Priority Matrix (MPM). In this framework, DSI corresponds to the normalized penalty score per kilometre and is used to compare inspection campaigns, while DRF and MPM extend the interpretation by identifying persistent defect families and translating measured degradation into maintenance priorities. The results show that degradation is non-uniform over time and defect-family-dependent, with cross-level and longitudinal-level defects dominating the cumulative penalty score. The DSI ranged from 467 points/km in September 2022 to 3183 points/km in March 2022, confirming a non-linear degradation and recovery pattern. The proposed indices provide an interpretable extension of the existing penalty-point framework and support condition-based maintenance planning on conventional railway lines. Full article
(This article belongs to the Section Infrastructures Inspection and Maintenance)
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30 pages, 2943 KB  
Article
A Quality-Aware Multimodal Reliability Framework for Health Assessment and Remaining Useful Life Prediction of Cold-Region Tunnels
by Boyang Liu, Jing Guan, Yi Yang and Wuer Ha
Infrastructures 2026, 11(8), 283; https://doi.org/10.3390/infrastructures11080283 - 10 Aug 2026
Viewed by 264
Abstract
This study proposes a quality-aware multimodal framework for health-state assessment and remaining useful life (RUL) prediction of cold-region tunnels. The framework integrates structural-response, environmental, apparent-defect, and engineering-inspectiondata, with the apparent-defect pathway jointly encoding raw images through a convolutional neural network and structured defect [...] Read more.
This study proposes a quality-aware multimodal framework for health-state assessment and remaining useful life (RUL) prediction of cold-region tunnels. The framework integrates structural-response, environmental, apparent-defect, and engineering-inspectiondata, with the apparent-defect pathway jointly encoding raw images through a convolutional neural network and structured defect variables. Five data-quality dimensions-completeness, accuracy, consistency, timeliness, and traceability are incorporated intoreliability-guided multimodal fusion. Their base weights were re-audited through two rounds of expert consultation, each comprising 323 valid questionnaires. The Cr-weighted group analytic hierarchy process yielded weights of 0.0548, 0.1326, 0.1372, 0.2279, and 0.4474, respectively, with a group consistency ratio of 0.0455; the ranking remained stable under one-at-a-time +10% perturbations. In the primary tunnel case study, the framework achieved 89.7% health-state accuracy, a 6.3% RUL mean absolute percentage error, and 84.1% accuracy under Gaussian perturbation of standardized numerical inputs at a noise scale of 0.15. To further examine the reliability contribution of data-quality information, an independent field panel comprising 600 segment-month observations from 25 segments across three operational tunnels was evaluated using target-excluded specifications, two-way fixed effects, leave-one-tunnel-out validation, multiple baseline models, and five fixed random seeds. A one-standard-deviation increase in lagged quality instability was associated with a 0.0151 increase in the subsequent state-error index (95% CI: 0.0118-0.0184; p < 0.001). In cross-tunnel random-forest tests, incorporating quality information increased mean R2 from 0.8277 to 0.8323 for state-error prediction and from 0.8517 to 0.8673 for RUL-contraction prediction, with both improvements significant in paired tests (p < 0.001). Split-conformal intervals achieved mean cross-tunnel coverage of 95.8% and 95.9%, respectively. These findings demonstrate that data-quality information provides a modest but statistically supported improvement in cross-tunnel reliability, whilethe principal contribution lies in integrating auditable data governance, reliability-aware fusion, and engineering decision support within a unified tunnel health-management framework. Full article
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22 pages, 5193 KB  
Article
Moisture Migration and Variation in Pile Shaft Resistance During Hydration-Heat-Induced Thawing–Refreezing Around Cast-in-Place Piles in Permafrost
by Zhilong Zhang, Tengbo Yu, Xuejun Liu and Jiyang Zhang
Infrastructures 2026, 11(8), 282; https://doi.org/10.3390/infrastructures11080282 - 10 Aug 2026
Viewed by 239
Abstract
The shaft resistance of cast-in-place piles in permafrost regions is commonly estimated using the initial moisture content and frozen-soil strength parameters obtained during site investigation. However, concrete hydration heat disturbs the temperature field of the surrounding frozen soil. During thawing and subsequent refreezing, [...] Read more.
The shaft resistance of cast-in-place piles in permafrost regions is commonly estimated using the initial moisture content and frozen-soil strength parameters obtained during site investigation. However, concrete hydration heat disturbs the temperature field of the surrounding frozen soil. During thawing and subsequent refreezing, this disturbance induces unfrozen-water migration and moisture redistribution. The resulting changes in the frozen pile–soil interface may cause the measured shaft resistance to deviate from the initial design estimate. In this study, laboratory direct shear tests and engineering-oriented reduced-scale pile–soil segment tests were conducted. The effects of initial moisture content and soil stratification on interface shear strength, the surrounding temperature field, and the post-test moisture distribution were investigated. Vertical pile compression tests were also performed to evaluate changes in pile shaft resistance. The main results are as follows. (1) The shear strength of the concrete–frozen-soil interface varied nonlinearly with moisture content. It increased initially and then decreased, reaching its maximum at a moisture content of 30%. (2) The temperature rise in the surrounding frozen soil was jointly controlled by soil stratification and initial moisture content. Higher moisture contents produced smaller peak temperature rises. In the near-pile region, the maximum difference in peak temperature among soil layers with different moisture contents was approximately 10.8%. (3) During thawing and refreezing, moisture migration was jointly affected by the temperature gradient and the moisture conditions of different soil layers. Unfrozen water migrated toward colder regions or lower-moisture soil layers under temperature gradients, capillary effects, and freezing suction, resulting in near-pile moisture depletion and localized moisture enrichment. For the Group A model, the initial-state estimate underestimated the measured peak shaft resistance by 12.45%. In contrast, for the layered B1 model, the initial-state estimate overestimated the measured peak shaft resistance by 20.58%. (4) A preliminary lumped equivalent coefficient, keq, was introduced to establish a relationship between moisture content and local equivalent interface resistance. After the measured post-test near-pile moisture distributions were incorporated into the calculation, the relative deviation decreased from 12.45% to 7.36% for Group A and from 20.58% to 9.66% for B1. Full article
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17 pages, 5684 KB  
Article
Synergistic Enhancement of Foamed Concrete Performance with Fibers and Additives Under Low-Temperature Environments and Mix Proportion Optimization
by Yufeng Xian, Yaning Zhang, Zunqing Liu, Haiwei Xie and Yifei Wang
Infrastructures 2026, 11(8), 281; https://doi.org/10.3390/infrastructures11080281 - 6 Aug 2026
Viewed by 235
Abstract
To address the technical challenges of hydration retardation and low early strength of foamed concrete in low-temperature environments of cold regions, this study investigated the effects of low-temperature curing (cycling between −5 °C and 5 °C) on the mechanical properties and microstructure of [...] Read more.
To address the technical challenges of hydration retardation and low early strength of foamed concrete in low-temperature environments of cold regions, this study investigated the effects of low-temperature curing (cycling between −5 °C and 5 °C) on the mechanical properties and microstructure of foamed concrete. Single-factor experiments were conducted to explore the effects of triethanolamine (TEA), urea, and polypropylene fibers (PPF) on the mechanical performance of foamed concrete. A response surface methodology (RSM) was employed to establish regression models between the dosages of each component and the compressive strength (CS), thereby determining the optimal mix proportion under low-temperature curing. The experimental results indicate that increasing the urea dosage leads to an increase in the flowability of foamed concrete, and the effects of the three types of admixtures on the CS all exhibit a non-linear characteristic that first increases and then decreases. The significance of the three factors on the CS of the material follows the order: TEA > PPF > urea. The obtained optimal mix proportion is 0.052% TEA, 1.08% urea, and 0.194% PPF, yielding 3, 7, and 28 d CS of 1.088 MPa, 1.342 MPa, and 2.301 MPa, respectively. Microstructural analysis via SEM observations and XRD analysis suggest that the admixtures effectively compensate for the hydration retardation induced by low temperatures, promoting the abundant generation of needle-like ettringite (AFt) and C-S-H gels that interweave into a dense network, thereby achieving higher strength. This study provides a theoretical basis and technical support for the low-temperature construction of foamed concrete subgrades in cold regions. Full article
(This article belongs to the Special Issue Cement-Based Materials for Infrastructure)
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17 pages, 61353 KB  
Article
Experimental Study on the Shear Performance of Headed Stud Connectors Under Axial Pressure in Steel–Concrete Composite Structures
by Nengrong Guo, Wuhao Huang, Duo Chen, Yunfeng Pan and Ning Hou
Infrastructures 2026, 11(8), 280; https://doi.org/10.3390/infrastructures11080280 - 6 Aug 2026
Viewed by 205
Abstract
Headed stud connectors are critical components for ensuring composite action in steel–concrete composite structures. In practical infrastructure applications, welded headed studs may be subjected not only to interface shear forces but also to additional compressive actions induced by structural restraint, self-weight, traffic effects, [...] Read more.
Headed stud connectors are critical components for ensuring composite action in steel–concrete composite structures. In practical infrastructure applications, welded headed studs may be subjected not only to interface shear forces but also to additional compressive actions induced by structural restraint, self-weight, traffic effects, and other service conditions. However, the influence of axial pressure applied along the stud height direction on the post-fatigue performance of stud-connected interfaces remains insufficiently understood. This study experimentally investigates the post-fatigue shear performance of welded headed stud connections subjected to different levels of axial pressure along the stud height direction. Nine push-out specimens were divided into three groups with axial pressures of 0, 20, and 40 kN applied to each specimen. A fatigue–static loading procedure was adopted, including initial static loading, intermittent static tests after every 400,000 fatigue cycles, and final static failure tests after two million fatigue cycles under a fatigue load range of 70–130 kN. The failure mode, load–slip response, total shear-transfer capacity, shear stiffness, and slip capacity were analyzed. The results show that all specimens maintained load-carrying capacity after two million fatigue cycles under the investigated loading conditions. The final failures were mainly characterized by shear failure at the root of the welded headed studs accompanied by local concrete crushing. Axial pressure improved the total shear-transfer capacity and shear stiffness of the stud-connected interfaces, while the ultimate slip capacity remained relatively stable. The enhancement is considered to be associated with improved interface interaction and local restraint effects, although these mechanisms cannot be independently quantified using the current test setup. These findings provide experimental evidence for evaluating welded headed stud connections subjected to combined axial pressure and fatigue loading within the investigated parameter range. Full article
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25 pages, 4282 KB  
Article
Wind-Induced Vibration Behaviour of Additional Conductor and Service Safety of Fittings in Railway Overhead Contact Lines Under Canyon Winds in Harsh Environments
by Tong Xing, Aobo Yang, Like Pan and Yang Song
Infrastructures 2026, 11(8), 279; https://doi.org/10.3390/infrastructures11080279 - 6 Aug 2026
Viewed by 239
Abstract
This study investigates the wind-induced vibration of railway overhead contact line additional conductors and the service safety of fittings in high-altitude canyon environments. Using the Dadu River canyon railway corridor as the engineering background, a three-dimensional stochastic wind field with temporal and spatial [...] Read more.
This study investigates the wind-induced vibration of railway overhead contact line additional conductors and the service safety of fittings in high-altitude canyon environments. Using the Dadu River canyon railway corridor as the engineering background, a three-dimensional stochastic wind field with temporal and spatial correlations is generated by combining the Kaimal, Tieleman and Panofsky spectra with a fourth-order autoregressive model. Wind-speed histories are converted into wind loads using quasi-steady aerodynamic theory. A geometrically nonlinear model based on the absolute nodal coordinate formulation is established, with suspension and support fittings represented by a swingable rod element and three-directional equivalent stiffness, respectively. Time-domain, rainflow-counting, power-spectral and parameter-sensitivity analyses are performed. Results show that fitting type strongly affects conductor-end response but has limited influence on mid-span vibration. Suspension fittings release wind-induced deformation through swinging, reducing stress peaks and high-amplitude cycles, whereas support fittings restrain end displacement but increase local constraint reactions and fatigue risk. The findings support fitting selection and wind-resistant fatigue design for additional conductors in canyon railway overhead contact lines. Full article
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28 pages, 1341 KB  
Article
Identifying Intersection Groups for Traffic Signal Coordination in Urban Road Networks: A Network Partitioning Approach
by Chenchen Kuai, Md Wahid Hasan, Po Tin Mak and Yunlong Zhang
Infrastructures 2026, 11(8), 278; https://doi.org/10.3390/infrastructures11080278 - 6 Aug 2026
Viewed by 323
Abstract
Multi-intersection traffic signal coordination and control improves urban traffic efficiency by coordinating signal timing across suitable corridors or groups of intersections. However, most existing work focuses on optimizing the signal timing plan for predefined intersection groups such as all intersections on an arterial, [...] Read more.
Multi-intersection traffic signal coordination and control improves urban traffic efficiency by coordinating signal timing across suitable corridors or groups of intersections. However, most existing work focuses on optimizing the signal timing plan for predefined intersection groups such as all intersections on an arterial, whereas the question of which intersections should be coordinated together for maximum efficiency is often overlooked. Moreover, as traffic patterns vary throughout the day, the most suitable intersection groups may not be fixed across different Time-of-Day (ToD) demand conditions. To address this gap, this study proposes a network partitioning approach to adaptively identify effective groups of intersections for traffic signal coordination. A Signal Coordination Network (SCN) is constructed to quantify the coordination benefit between intersections based on traffic volume, spatial proximity, and cycle length compatibility. By solving a maximum set-packing problem, the proposed approach partitions the SCN into the most effective intersection groups, including isolated intersections, arterial progression groups, and network progression groups. Compared with the best-performing baselines, the proposed method reduces average travel time by 2.7% and average delay by 7.0% across the tested network–ToD scenarios, while the experiments under demand variation show statistically significant improvements in all AM and PM peak scenarios and comparable performance during off-peak periods. These results suggest that explicit coordination-group selection can provide additional operational benefits beyond local retiming, adaptive control, and predefined corridor or network coordination. The proposed framework offers a practical planning-level tool for designing ToD-sensitive signal coordination plans in urban networks. Full article
(This article belongs to the Special Issue Smart Mobility and Transportation Infrastructure)
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40 pages, 4737 KB  
Review
Distributed Fiber Optic Sensors (DFOSs) for Structural Health Monitoring (SHM) of Railway Infrastructure: A Critical Review
by Shima Taheri, Mohammad Siahkouhi, Ali Moghimi and Maria Rashidi
Infrastructures 2026, 11(8), 277; https://doi.org/10.3390/infrastructures11080277 - 5 Aug 2026
Viewed by 502
Abstract
Distributed fiber optic sensing (DFOS) has emerged as a transformative technology for structural health monitoring (SHM) of railway infrastructure, offering continuous, high-resolution measurements along extended optical fiber lengths, capabilities that conventional point sensors such as strain gauges and accelerometers cannot match. This review [...] Read more.
Distributed fiber optic sensing (DFOS) has emerged as a transformative technology for structural health monitoring (SHM) of railway infrastructure, offering continuous, high-resolution measurements along extended optical fiber lengths, capabilities that conventional point sensors such as strain gauges and accelerometers cannot match. This review critically examines DFOS technology and its railway SHM applications, covering system components, interrogator units, optical fiber cables, and data acquisition systems, alongside the three principal scattering mechanisms: Rayleigh, Brillouin, and Raman, each offering distinct trade-offs in spatial resolution, sensing range, and measurand sensitivity. Field applications across track and sleeper monitoring, bridge health evaluation, tunnel lining assessment, and embankment stability are reviewed and critically compared. The integration of artificial intelligence (AI) and machine learning (ML) with DFOS data streams is discussed, demonstrating detection accuracy exceeding 97% in recent studies. Its main application rail embankment monitoring is discussed. Key challenges are identified, including high interrogator costs, large data volumes, installation complexity in retrofit scenarios, and environmental noise under operational train speeds. Future research priorities include lower-cost interrogation hardware, automated signal processing pipelines, digital twin integration, and standardized performance frameworks to accelerate large-scale adoption across railway networks worldwide. Full article
(This article belongs to the Special Issue Advances in Computer Vision, Object Detection, and AI Technologies for Infrastructures)
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18 pages, 1920 KB  
Article
Analyzing Interrelationships Between Asphalt Pavement Distresses Across Multiple Lebanese Regions Using Association Rule Mining for Enhanced Maintenance Strategies
by Mariam Tabaja, Farah Homsi and Muhsin Elie Rahhal
Infrastructures 2026, 11(8), 276; https://doi.org/10.3390/infrastructures11080276 - 5 Aug 2026
Viewed by 410
Abstract
Pavement distresses play a pivotal role in evaluating roadway conditions and guiding maintenance strategies. In many instances, these distresses arise from construction deficiencies, substandard material quality, or inadequate maintenance practices, rather than from inherent design shortcomings. Understanding the interrelationships among different types of [...] Read more.
Pavement distresses play a pivotal role in evaluating roadway conditions and guiding maintenance strategies. In many instances, these distresses arise from construction deficiencies, substandard material quality, or inadequate maintenance practices, rather than from inherent design shortcomings. Understanding the interrelationships among different types of pavement distress is therefore essential for engineers and decision-makers seeking to enhance pavement performance and prolong service life. This study examines the statistical associations among various asphalt pavement distresses using association rule mining techniques. The dataset was collected from 18 regions in Lebanon, covering a total roadway length of 419.87 km, thereby enabling a comprehensive analysis. Ten primary categories of pavement distress were identified and analyzed using support, confidence, and Lift measures to quantify their co-occurrence patterns and dependency relationships. The results revealed strong statistical associations among most pavement distress types, with particularly strong interactions involving raveling and weathering, longitudinal cracking, alligator cracking, patching, and potholes. Raveling and weathering were the most prevalent distress factors, accounting for 20.88% of total occurrences, whereas block cracking was the least frequent, representing only 0.34%. The joint probability of raveling and weathering occurring with alligator cracking reached 37.38%, while the conditional probability of alligator cracking given the presence of raveling and weathering was 47.65%. Lift analysis further distinguished associations that were stronger than expected based on distress prevalence alone, thereby reducing the influence of frequency-driven relationships. These findings demonstrate the effectiveness of the proposed analytical framework in identifying statistically grounded pavement distress interactions and provide complementary information to support pavement management, maintenance prioritization, and resource allocation. Full article
(This article belongs to the Section Infrastructures Inspection and Maintenance)
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21 pages, 10222 KB  
Article
Experimental Investigation on Water-Sensitive Engineering Behaviors of High-Fines Clayey Sand and Quantitative Correlations Between Physical and Mechanical Indices
by Dayu Yang, Rencheng Ye, Zejun Song, Xiaohong Wang, Qingzheng Yang and Tiande Wen
Infrastructures 2026, 11(8), 275; https://doi.org/10.3390/infrastructures11080275 - 5 Aug 2026
Viewed by 292
Abstract
Clayey sand is a typical transitional coastal alluvial soil controlled by both coarse-grain friction and fine-grain cementation. Current studies focus mostly on remolded samples, lacking systematic understanding of water-induced structural degradation and quantitative physico-mechanical correlations for natural undisturbed clayey sand. In this work, [...] Read more.
Clayey sand is a typical transitional coastal alluvial soil controlled by both coarse-grain friction and fine-grain cementation. Current studies focus mostly on remolded samples, lacking systematic understanding of water-induced structural degradation and quantitative physico-mechanical correlations for natural undisturbed clayey sand. In this work, 74 intact undisturbed specimens (0.5–23.0 m depth) were tested via basic physical tests, one-dimensional consolidation and consolidated-undrained triaxial shear tests. Pearson correlation analysis was performed to establish prediction relationships between routine physical indices and mechanical parameters. Results show the soil is classified as SC clayey sand with 39.70% fines and an average natural water content of 23.17%. Natural water content dominates soil engineering performance, presenting strong linear correlations with dry density and void ratio (|r| = 0.90). Higher water content and void ratio increase compressibility and reduce shear strength. The compression coefficient and compression modulus exhibited a consistent nonlinear relationship, reflecting the inherent linkage between these two compression parameters. Burial depth has little influence on soil properties, and plasticity index only serves for soil classification. Mechanistically, increasing moisture may thicken adsorbed water films, weaken interparticle contact and matric suction, and the fine particle-filled skeleton may further enhance the water sensitivity of the soil. The established prediction models support fast evaluation of soil mechanical behaviors, offering theoretical and practical support for geotechnical design of similar coastal clayey sand strata. Full article
(This article belongs to the Special Issue Resilience and Sustainability in Geotechnical Infrastructure)
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19 pages, 3980 KB  
Article
A Structural–Visual Integrated Evaluation Framework for Seismic Damage Assessment of RC Double-Column Piers
by Zhibing Yu, Zixin Xu, Chao Zhang, Yongjun Zhou, Xiaojun Hou, Longqi Zhang, Wang Liao and Haiou Li
Infrastructures 2026, 11(8), 274; https://doi.org/10.3390/infrastructures11080274 - 4 Aug 2026
Viewed by 218
Abstract
The post-earthquake damage state of reinforced concrete (RC) double-column piers directly affects bridge traffic capacity and emergency response efficiency. To improve the interpretability of damage assessment, this study proposes a Structural–Visual Integrated Evaluation (SVIE) framework that combines structural response analysis with image-based damage [...] Read more.
The post-earthquake damage state of reinforced concrete (RC) double-column piers directly affects bridge traffic capacity and emergency response efficiency. To improve the interpretability of damage assessment, this study proposes a Structural–Visual Integrated Evaluation (SVIE) framework that combines structural response analysis with image-based damage evidence. Structural responses from quasi-static tests are used to define four baseline damage states: intact-to-slight, moderate, severe, and critical damage. An improved DeepLabv3+ model is then applied to 315 global-scene images for end-to-end semantic segmentation of background, concrete spalling, and reinforcement exposure. The extracted visual evidence is used to verify its consistency with the baseline structural states. On the test set, the model effectively identified concrete spalling regions, achieving an IoU, F1-score, Precision, and Recall of 78.86%, 88.18%, 89.93%, and 86.49%, respectively. For reinforcement exposure, although IoU and Recall were relatively low because of sample scarcity and small-target characteristics, Precision reached 70.40%, indicating that detected regions can provide supplementary evidence for severe local damage. The consistency analysis showed that the morphology of visual damage was generally compatible with the progression of structural damage states. The results provide a laboratory-based proof of concept for a mechanically grounded and visually interpretable framework for rapid post-earthquake assessment of RC double-column piers. Full article
(This article belongs to the Section Infrastructures and Structural Engineering)
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7 pages, 587 KB  
Editorial
Earthquake and Multi-Hazard Resilience: Community-Level Insights and AI/ML Applications
by Mojtaba Harati, John W. van de Lindt and Maria Koliou
Infrastructures 2026, 11(8), 273; https://doi.org/10.3390/infrastructures11080273 - 4 Aug 2026
Viewed by 316
Abstract
Earthquake resilience is increasingly a problem of interactions across hazards, assets, infrastructure systems, and recovery processes [...] Full article
(This article belongs to the Special Issue Earthquake and Multi-Hazard Resilience: Community-Level Insights and AI/ML Applications)
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30 pages, 41798 KB  
Article
Axial Behaviour of Reinforced Concrete Columns Strengthened with Self-Compacting Geopolymer Concrete Jacketing and External FRP Confinement
by Talal Athobaiti, Osama Youssf, Mohamed Mortagi and Ahmed M. Tahwia
Infrastructures 2026, 11(8), 272; https://doi.org/10.3390/infrastructures11080272 - 3 Aug 2026
Viewed by 243
Abstract
The structural performance of reinforced concrete (RC) columns can be substantially improved by using advanced confinement systems and sustainable cementitious materials. This study presents an integrated experimental, numerical, and analytical investigation of RC columns strengthened with self-compacting geopolymer concrete (SCGC) and carbon fiber-reinforced [...] Read more.
The structural performance of reinforced concrete (RC) columns can be substantially improved by using advanced confinement systems and sustainable cementitious materials. This study presents an integrated experimental, numerical, and analytical investigation of RC columns strengthened with self-compacting geopolymer concrete (SCGC) and carbon fiber-reinforced polymer (CFRP) under axial compression. Twelve column specimens were tested across four groups: conventional RC columns, unconfined SCGC columns, SCGC-jacketed columns (CONF. SCGC), and CFRP-wrapped columns (CONF. FRP), in three cross-sectional geometries: square (177 × 177 mm), rectangular (265 × 177 mm), and circular (Ø200 mm). Experimental results demonstrated that replacing conventional concrete with SCGC improved deformation capacity, increasing the ultimate axial displacement from 2.07 mm in the reference square RC column (RC C1) to 3.21 mm in the corresponding square SCGC specimen (SCGC C1). On a normalized stress basis, the unconfined SCGC specimens achieved ultimate axial stress values of 56–64 MPa compared to 41–49 MPa for the RC reference columns of the same geometry, representing material-level strength gains of 1.30–1.49×. The application of external confinement further enhanced column behaviour. The CFRP-wrapped specimens achieved the highest material-level strength efficiency, with normalized ultimate axial stress values of 98–108 MPa for the square and rectangular geometries, representing gains of 2.01–2.41× over the corresponding unconfined RC columns of identical cross-section. The SCGC-jacketed specimens achieved the highest absolute load capacities, with CONF. SCGC C2 reaching 8270 kN and a maximum stiffness of 5531 kN/mm and energy absorption of 19,740 kN·mm. However, on a normalized stress basis, the SCGC-jacketed square and rectangular specimens achieved 45–47 MPa, comparable to the RC reference columns, confirming that their absolute load gains are primarily attributable to section enlargement rather than intrinsic material strength enhancement. The circular SCGC-jacketed specimen achieved a normalized stress of 43 MPa, consistent with the same trend. A three-dimensional nonlinear finite element model developed in ABAQUS using the Concrete Damaged Plasticity model and cohesive zone interactions showed close agreement with experimental results, with mean prediction ratios of 1.03 for ultimate load and 0.96 for displacement. An analytical model provided conservative estimates of axial capacity. The findings demonstrate that CFRP wrapping offers superior material-level confinement efficiency, while SCGC jacketing provides the highest absolute load capacity through combined section enlargement and passive confinement, representing a potentially more environmentally friendly strengthening strategy for existing RC columns. Full article
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32 pages, 32794 KB  
Article
PCA–GPR-Assisted Sequential Bayesian Inversion of Slope Mechanical Parameters from Multi-Stage Deep Horizontal Displacement Monitoring
by Youyun Li, Xu Chen, Zaiyang Yu and Wangyu Wu
Infrastructures 2026, 11(8), 271; https://doi.org/10.3390/infrastructures11080271 - 3 Aug 2026
Viewed by 251
Abstract
Reliable mechanical parameters are needed to predict deformation during staged slope excavation, yet deep inclinometer profiles are high-dimensional and repeated numerical inversion is costly. This study integrates principal component analysis, Gaussian process regression (GPR), and sequential Bayesian updating to infer six effective parameters [...] Read more.
Reliable mechanical parameters are needed to predict deformation during staged slope excavation, yet deep inclinometer profiles are high-dimensional and repeated numerical inversion is costly. This study integrates principal component analysis, Gaussian process regression (GPR), and sequential Bayesian updating to infer six effective parameters from multi-stage horizontal displacement profiles of a highway slope in Shaoyang, China. FLAC3D simulations were performed for a 120-point Latin hypercube design. Three principal components explained over 99% of profile variance. Ten repetitions of five-fold cross-validation yielded mean PCA–GPR R2 values of 0.9769–0.9945. Empirical coverages of the 95% marginal prediction intervals ranged from 93.2% to 96.0%, and the GPR predictive covariance was included in the likelihood. Truncated multivariate Gaussian approximations were used to transfer posterior means and covariance structures between excavation stages. A five-strategy comparison showed that adding Stage 3 reduced parameter standard deviations by 10.0–22.0%, followed by a further 11.1–32.2% reduction after Stage 4. Sensitivity and posterior-contraction analyses indicated stronger constraints on the stiffness parameters, whereas ϕ2 remained weakly identifiable. The final posterior means reproduced the spatially held-out, within-site CX-2 profile with a mean absolute error of 0.18 mm. The framework quantifies the parameter-specific information gained from complete profiles across excavation stages; its findings remain conditional on the monitored site and the adopted modeling and error assumptions. Full article
(This article belongs to the Topic Dams, Levees, Hydraulic Structures, and Hydropower)
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27 pages, 3173 KB  
Review
Generative AI in Transportation Analytics: Foundations, Deep Generative Models, and Practical Use Cases
by Samrad Babaee, Mohsen Naghdi, Ali Mansouri and Abdolmajid Erfani
Infrastructures 2026, 11(8), 270; https://doi.org/10.3390/infrastructures11080270 - 3 Aug 2026
Viewed by 507
Abstract
Generative artificial intelligence (Gen-AI) is rapidly reshaping transportation analytics by enabling data synthesis, uncertainty modeling, and decision-oriented reasoning under sparse and complex conditions. This study presents a systematic, use-case-driven review of 142 empirical transportation studies published between 2018 and August 2025, synthesizing how [...] Read more.
Generative artificial intelligence (Gen-AI) is rapidly reshaping transportation analytics by enabling data synthesis, uncertainty modeling, and decision-oriented reasoning under sparse and complex conditions. This study presents a systematic, use-case-driven review of 142 empirical transportation studies published between 2018 and August 2025, synthesizing how Gen-AI has been adapted, evaluated, and integrated into real transportation workflows. This study distinguishes two dominant model families, deep generative models and foundation models, and show that they serve fundamentally different yet increasingly complementary roles: the former addressing data scarcity and distributional learning, and the latter enabling reasoning, coordination, and decision support. Through bibliometric analysis, topic modeling, and application mapping, this study identifies major adoption trends, persistent limitations, and research gaps related to generalization, validation, scalability, and trust. The findings reveal a field-level shift from prediction-centric modeling toward generative, decision-aware transportation intelligence and outline a roadmap for responsible, domain-grounded deployment of Gen-AI in safety-critical transportation systems. Full article
(This article belongs to the Special Issue Advances in Computer Vision, Object Detection, and AI Technologies for Infrastructures)
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19 pages, 13789 KB  
Article
Characterization of Surface-Breaking Cracks in Concrete Using Ultrasonic Imaging
by Suhaib Ul Reyaz, Hao Wang and Husam Najm
Infrastructures 2026, 11(8), 269; https://doi.org/10.3390/infrastructures11080269 - 3 Aug 2026
Viewed by 346
Abstract
Surface-breaking cracks in concrete structures can accelerate deterioration by facilitating the ingress of moisture, chlorides, and other aggressive agents. Reliable characterization of crack depth is therefore essential for structural health monitoring and maintenance of concrete infrastructure. This study presents an ultrasonic common midpoint [...] Read more.
Surface-breaking cracks in concrete structures can accelerate deterioration by facilitating the ingress of moisture, chlorides, and other aggressive agents. Reliable characterization of crack depth is therefore essential for structural health monitoring and maintenance of concrete infrastructure. This study presents an ultrasonic common midpoint (CMP)-based approach for crack-tip localization and crack-depth characterization in concrete. Ultrasonic measurements were acquired using a pitch-catch configuration in which the transmitter and receiver were positioned symmetrically on both sides of surface crack while maintaining a fixed midpoint. Measurements obtained at multiple transmitter–receiver separations were processed to extract the time-of-arrival (ToA) associated with crack-tip diffraction. The measured ToAs were subsequently used within a travel-time-based localization framework to generate crack-tip images and estimate crack-tip coordinates. The proposed methodology was evaluated on concrete slabs containing vertical and inclined surface-breaking cracks of varying depths. In addition, the approach was applied to a reinforced concrete beam specimen containing thin cracks caused by flexural loading. The localized crack-tip positions from ultrasonic imaging are in good agreement with the observed crack depths and geometries. The proposed method offers a non-destructive approach for crack-tip localization and crack-depth characterization in concrete and may support condition assessment of concrete infrastructure. Full article
(This article belongs to the Special Issue Advances in Structural Health Monitoring and Industry 5.0 Innovations for Bridge Management and Conservation)
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17 pages, 4100 KB  
Article
Hydration and Microstructural Evolution of Cement Pastes Incorporating Submerged Arc Welding Slag
by Carlos Rodríguez, Fernando Fernández, Marina Sánchez, Pablo Gómez, Miriam Hernández and Isidro Sánchez
Infrastructures 2026, 11(8), 268; https://doi.org/10.3390/infrastructures11080268 - 1 Aug 2026
Viewed by 274
Abstract
The valorisation of industrial by-products as supplementary cementitious materials is a promising strategy to reduce clinker consumption and improve the sustainability of cement-based materials. In this study, the influence of submerged arc welding (SAW) slag on the hydration behaviour and microstructural evolution of [...] Read more.
The valorisation of industrial by-products as supplementary cementitious materials is a promising strategy to reduce clinker consumption and improve the sustainability of cement-based materials. In this study, the influence of submerged arc welding (SAW) slag on the hydration behaviour and microstructural evolution of cement pastes was investigated. Two SAW slags from different industrial sources were incorporated as partial replacements of ordinary Portland cement at 5%, 15%, and 30% by mass. Cement pastes were prepared with water-to-binder ratios of 0.3 and 0.4 and characterised through setting time, water demand, mercury intrusion porosimetry (MIP), differential scanning calorimetry (DSC), and X-ray diffraction (XRD). The results showed that SAW slag systematically delayed both initial and final setting times, while having only a negligible effect on water demand. Under the fixed mix conditions adopted in this study, this retardation is interpreted as the combined effect of clinker dilution and modified fresh-state conditions. MIP analysis revealed higher early-age porosity in SAW-containing pastes, particularly at high replacement levels and higher water-to-binder ratios, although mixtures with up to 15% slag approached the reference pore structure at later ages. Thermal analysis indicated lower bound water and portlandite contents at early ages, mainly due to clinker dilution, while long-term hydration development remained comparable at moderate replacement levels. At higher slag contents, some mixtures showed higher calcium carbonate contents, suggesting a tendency toward increased carbonate formation under the investigated conditions. Overall, the results indicate that SAW slag primarily affected early paste behaviour and pore structure development, with clinker dilution appearing to be the main mechanism, although weak secondary physical or chemical contributions cannot be completely excluded. Full article
(This article belongs to the Special Issue Recent Advances in Enhancing Sustainability and Durability of Cement and Concrete)
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22 pages, 8206 KB  
Article
Non-Uniform Shear Deformation and Its Influence Factor Sensitivity of Colluvial Coarse-Grained Soil
by Yonglong Qu, Xinglong Wang, Gengshe Yang, Yanhu Mu, Lizhen Wu, Tengfei Han and Mengyuan Zhang
Infrastructures 2026, 11(8), 267; https://doi.org/10.3390/infrastructures11080267 - 1 Aug 2026
Viewed by 301
Abstract
To investigate the mechanical properties of coarse-grained soil in high-altitude mountainous areas, experimental research was conducted to explore the shear process, shear modulus, shear dilation, and stress axis rotation of coarse-grained soil under varying conditions. The sensitivity and mechanisms of these factors were [...] Read more.
To investigate the mechanical properties of coarse-grained soil in high-altitude mountainous areas, experimental research was conducted to explore the shear process, shear modulus, shear dilation, and stress axis rotation of coarse-grained soil under varying conditions. The sensitivity and mechanisms of these factors were also analyzed. The results indicate that increasing water content and fine particle content significantly diminish the strain-hardening characteristic, whereas dry density and normal stress augment this effect. Under shear stress, the samples exhibit pronounced non-uniform shear dilatancy. Elevated water content, fine particle content, and normal stress enhance shear contraction at the rear of the sample while suppressing shear dilation at the front. In contrast, dry density produces the opposite effect. The rotation of the stress axis initially follows a nonlinear growth pattern before transitioning to linear growth. The growth rate and ultimate rotation angle increase monotonically with water content, fine particle content, and normal stress but decrease with increasing dry density. Additionally, the shear modulus decreases exponentially with increasing water content and increases exponentially with dry density, fine particle content, and normal stress. Ultimately, normal stress is identified as the most sensitive factor, followed by dry density and fine particle content, with water content being the least sensitive. These findings can provide geotechnical parameters and a theoretical basis for the scientific prevention of high-altitude geological hazards. These findings can provide indoor mechanical parameters and deformation laws of coarse-grained soils for engineering. Full article
(This article belongs to the Special Issue Construction and Maintenance of Transportation Infrastructure in Extreme Environments)
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25 pages, 1371 KB  
Article
Evaluating Risky Driving Behavior Using a Naturalistic Driving Dataset: A Hybrid Modelling Approach
by Eleni Maria Theodoraki, Thodoris Garefalakis, Eva Michelaraki and George Yannis
Infrastructures 2026, 11(8), 266; https://doi.org/10.3390/infrastructures11080266 - 1 Aug 2026
Viewed by 357
Abstract
Driver behavior is a critical factor in road safety, contributing to the majority of traffic crashes. The i-DREAMS project introduced the concept of a Safety Tolerance Zone (STZ) to enhance driving safety through real-time and post-trip interventions. This study develops and evaluates three [...] Read more.
Driver behavior is a critical factor in road safety, contributing to the majority of traffic crashes. The i-DREAMS project introduced the concept of a Safety Tolerance Zone (STZ) to enhance driving safety through real-time and post-trip interventions. This study develops and evaluates three hybrid machine learning models—(i) Deep Neural Network–Random Forest (DNN-RF), (ii) Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM), and (iii) Recurrent Neural Network–AdaBoost (RNN-AdaBoost)—to classify risky driving behavior into three safety levels using naturalistic driving data from Belgium and the UK. The dataset includes 69 drivers, 15,389 trips, and 265,512 min of driving data. Among the models tested, the DNN-RF model demonstrated the highest accuracy, reaching 98% in Belgium and 97% in the United Kingdom, outperforming other approaches. Feature importance analysis identified harsh acceleration and braking as the most critical factors in Belgium, while total trip distance and harsh acceleration were predominant in the UK. To enhance model transparency, we applied the Local Interpretable Model-agnostic Explanations (LIME) algorithm, providing valuable insights into model predictions. The findings support the potential of hybrid deep learning models in improving road safety by accurately detecting risky driving behaviors. These insights can inform targeted interventions and driver assistance technologies to mitigate crash risks and promote safer driving practices. Full article
(This article belongs to the Special Issue Safer Roads Ahead: Exploring the Latest Innovations and Advancements in Road Design and Safety Technology, 2nd Edition)
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20 pages, 7709 KB  
Article
Human-Centered Optimization of Expressway Interchange Guide-Sign Infrastructure in Complex Road Networks: Evidence from Visual Behavior, Physiological Responses, and Driving Simulation
by Yanshuang Zhi, Shuzhen Lou, Hongfu Wu, Yingying Luo and Yanqun Yang
Infrastructures 2026, 11(8), 265; https://doi.org/10.3390/infrastructures11080265 - 31 Jul 2026
Viewed by 362
Abstract
Expressway interchange guide signs must support rapid route decisions under limited viewing time, high information load, and potential vehicle occlusion. This study developed a human-centered framework for optimizing guide-sign information and presentation in complex expressway networks by integrating an on-road field experiment, driving [...] Read more.
Expressway interchange guide signs must support rapid route decisions under limited viewing time, high information load, and potential vehicle occlusion. This study developed a human-centered framework for optimizing guide-sign information and presentation in complex expressway networks by integrating an on-road field experiment, driving simulation, eye-movement measures, physiological responses, and driving behavior. The field experiment involved ten drivers unfamiliar with the Fuzhou expressway network and yielded 119 valid sign-reading episodes. A separate simulation experiment with 60 licensed drivers examined character heights of 55, 65, and 75 cm and roadside, median-side, and dual-side installation under three ordinal traffic background conditions. Drivers generally initiated visual search in the upper-left region of sign panels and focused on route-relevant place names and directional arrows. Descriptive observations suggested that usable control-point information could support route inference when the destination was absent, whereas the absence of both destination and usable control-point information was accompanied by more sustained deceleration and more dispersed visual search. Effective viewing distance increased across the tested character-height conditions. At a design speed of 120 km/h and an assumed recognition time of 2.6 s, the required viewing distance was approximately 86.7 m. The mean viewing distance for the 55 cm condition was below this requirement, whereas the 65 and 75 cm conditions exceeded it. The findings were applied to Xiuzhai Interchange. In a dynamic screen-based route-choice test, 24 of 25 participants made acceptable choices. Overall, guide signs in complex networks should be designed as coordinated information systems integrating control-point hierarchy, information continuity, character height, and installation position. Full article
(This article belongs to the Special Issue Safer Roads Ahead: Exploring the Latest Innovations and Advancements in Road Design and Safety Technology, 2nd Edition)
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25 pages, 8448 KB  
Article
Micro Breakage-Induced Contact Network Evolution and Performance Degradation of Graded Aggregates Under Cyclic Loading
by Xianpu Xiao, Qian Zhang, Kang Wang, Kang Xie, Shengjun Zhang, Tao Ding and Shiqiang Zhang
Infrastructures 2026, 11(8), 264; https://doi.org/10.3390/infrastructures11080264 - 30 Jul 2026
Viewed by 232
Abstract
Graded aggregates used in high-speed railway subgrades often undergo micro breakage (i.e., corner and edge spalling) under cyclic loading, whereas the mechanism linking local particle spalling to contact-network evolution and permanent deformation remains unclear. To address the lack of a physically based micro [...] Read more.
Graded aggregates used in high-speed railway subgrades often undergo micro breakage (i.e., corner and edge spalling) under cyclic loading, whereas the mechanism linking local particle spalling to contact-network evolution and permanent deformation remains unclear. To address the lack of a physically based micro breakage criterion and a continuous local shape-updating scheme in existing DEM approaches, this study proposes an energy-driven micro breakage method. The method uses the relationship between contact elastic energy and fracture energy for newly created free surfaces as the breakage criterion. It also employs a radial function representation to simulate local particle shape evolution. The proposed method is subsequently validated through multilevel tests and shows reasonable agreement with the experimental results for particle micro breakage and the associated mechanical responses. Furthermore, dynamic triaxial simulation results show that micro breakage exhibits a distinct cumulative characteristic and increases with loading frequency and amplitude. Meanwhile, micro breakage drives particle rearrangement and new contact formation, leading to skeleton densification and weakening of the strong force-chain network, which in turn accelerates plastic deformation accumulation. These coupled processes constitute an important mechanism governing the progressive degradation of graded aggregates under cyclic loading. This study clarifies the associated particle-scale mechanism and provides a reference for performance optimization. Full article
(This article belongs to the Section Infrastructures Materials and Constructions)
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