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Evidence of a Highway–Rail Grade Crossing Safety Plateau Through Regime-Transition Analysis and Explainable Machine Learning
Ontosaturation: A Novel Ontological Mechanism for Property Completeness Validation in Building Information Modeling (BIM)

Journal Description

Infrastructures

Infrastructures is an international, scientific, peer-reviewed open access journal on infrastructures published monthly online by MDPI. Infrastructures is affiliated to International Society for Maintenance and Rehabilitation of Transport Infrastructures (iSMARTi) and their members receive a discount on the article processing charges.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, ESCI (Web of Science), Inspec, and other databases.
Journal Rank: JCR - Q2 (Construction and Building Technology) / CiteScore - Q1 (Building and Construction)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 18.2 days after submission; acceptance to publication is undertaken in 2.9 days (median values for papers published in this journal in the first half of 2026).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Journal Cluster of Civil Engineering and Built Environment: Acoustics, Architecture, Buildings, CivilEng, Construction Materials, Infrastructures, Intelligent Infrastructure and Construction, NDT and Vibration.

Impact Factor: 3.6 (2025); 5-Year Impact Factor: 3.5 (2025)

Imprint Information Journal Flyer Open Access ISSN: 2412-3811

Latest Articles

18 pages, 4015 KB

Open AccessArticle

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

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

Open AccessArticle

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

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

Open AccessArticle

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

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

Open AccessArticle

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

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

Open AccessArticle

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

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

Open AccessArticle

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

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

Open AccessArticle

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

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

Open AccessArticle

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

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

Open AccessArticle

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

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

Open AccessArticle

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

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

Open AccessArticle

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

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, k_eq, 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

Open AccessArticle

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

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

Open AccessArticle

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

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

Open AccessArticle

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

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

Open AccessArticle

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

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

Open AccessReview

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

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

Open AccessArticle

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

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

Open AccessArticle

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

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

Open AccessArticle

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

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

Open AccessEditorial

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

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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