Infrastructures

18 pages, 895 KB

Open AccessArticle

A Multi-Task Temporal Fusion Framework for 48 h Ahead Joint Prediction of Dam Crack Responses and Rebar Stress from Multi-Source Monitoring Data

by Binbin Liu, Mingming Wang, Xiaolei Zhu and Wanbo Zhang

Infrastructures 2026, 11(6), 202; https://doi.org/10.3390/infrastructures11060202 (registering DOI) - 15 Jun 2026

Abstract

Crack opening and reinforcement stress are two complementary indicators of the service state of reinforced concrete hydraulic structures, yet they are often predicted separately. This study develops a data-driven multi-task temporal fusion framework for joint 48 h ahead prediction of dam crack responses [...] Read more.

Crack opening and reinforcement stress are two complementary indicators of the service state of reinforced concrete hydraulic structures, yet they are often predicted separately. This study develops a data-driven multi-task temporal fusion framework for joint 48 h ahead prediction of dam crack responses and rebar stress using multi-source monitoring data. The measured data comprise five crack-monitoring series, five rebar stress series, local temperature channels, reservoir water level, antecedent rainfall, and an auxiliary environmental signal over approximately four years. Target responses are aligned only at common measured timestamps; no synthetic target observations are introduced. A simplified engineering layout and plan-based crack–rebar distances are further used to examine whether an explicit spatial prior can strengthen the shared temporal representation without introducing synthetic target values. A residual multi-task temporal fusion network (MTTF-Net) is proposed with a shared Transformer encoder, attention pooling, task-specific decoders, and a response-continuity regularization term. The model is compared with persistence, Ridge regression, random forest, Extra Trees, XGBoost, and GRU baselines under a chronological train/validation/test split. For the independent test period, Ridge regression obtains the lowest overall RMSE (2.2968), whereas MTTF-Net provides the lowest crack RMSE (0.0141), the lowest overall MAE (1.0035), and the second-best overall RMSE (2.3813). Distance-informed ablation, denoted as MTTF-Net-S, remains close to MTTF-Net in macro-averaged

R^{2}

but is not superior in the overall test metrics, indicating that the available horizontal distances are valuable engineering metadata but cannot replace richer three-dimensional structural connectivity. These results indicate that the monitoring data contain a strong linear autoregressive component, while multi-task temporal fusion improves nonlinear crack response prediction and remains competitive for stress forecasting. The source code is prepared as a public implementation package, whereas the measured monitoring dataset is subject to data owner restrictions. Full article

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

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19 pages, 4198 KB

Open AccessArticle

Application of GCN-MGWR for Spatial–Temporal Analysis of Pavement Damages in Permafrost Regions Along the Qinghai–Xizang Highway, China

by Liqiong Li, Changjie Yao, Mingtang Chai and Shuhong Wang

Infrastructures 2026, 11(6), 201; https://doi.org/10.3390/infrastructures11060201 (registering DOI) - 12 Jun 2026

Abstract

Pavement damages along the Qinghai–Xizang Highway (QXH) in permafrost regions are jointly controlled by geographical and engineering factors, leading to higher damage rates than in non-permafrost regions. However, the overall development trend of these damages and the spatial–temporal patterns have not been systematically [...] Read more.

Pavement damages along the Qinghai–Xizang Highway (QXH) in permafrost regions are jointly controlled by geographical and engineering factors, leading to higher damage rates than in non-permafrost regions. However, the overall development trend of these damages and the spatial–temporal patterns have not been systematically quantified. To analyze the spatial distribution of different pavement damages, reveal the spatial–temporal associations, and analyze the spatial heterogeneity of the driving factors, three field surveys were conducted in 2014, 2019 and 2024, with records of seven major pavement damages. Statistical analyses were used to examine the relationships among single and co-occurring damages. Then, a novel geographical model, combining a graph convolutional network with multi-scale geographically weighted regression (GCN-MGWR), was further developed to treat the QXH as a linear geographic unit and to assess the spatial heterogeneity and relative contribution of different influencing factors. The results show that the mean pavement damage ratios in permafrost regions during the three surveys are 4.21%, 6.82%, and 4.74%, respectively, with crack-type damages (transverse, longitudinal, and block cracking) exhibiting the highest occurrence rates. The three strongest pairs of correlations are transverse and longitudinal cracking (0.584), transverse and block cracking (0.570), and waving and rutting (0.622). The primary factors influencing crack-type damages are embankment thickness, mean annual ground surface temperature (MAGST), elevation and existing damages. Transverse and longitudinal cracking show a pronounced increase with rising MAGST, and embankment thickness below 1 m or above 4 m significantly contribute to the development of both crack types (SHAP > 0.5). Overall, the evolution of crack-type damages has shifted from being primarily controlled by geographical factors to being controlled by the combined influence of engineering and geographical factors during 2014–2024. The factor contributions identified by the GCN-MGWR model provide quantitative support for the regional adaptive design and specific maintenance of roadway in permafrost regions. Full article

20 pages, 11392 KB

Open AccessArticle

Machine Learning-Based Road Surface Defect Detection from Signal Features Using Data from an Instrumented Vehicle Platform

by Berkin Uluutku, Korkut Kaynardag, Daisuke Oshima, John Cotter and Fikret Necati Catbas

Infrastructures 2026, 11(6), 200; https://doi.org/10.3390/infrastructures11060200 (registering DOI) - 12 Jun 2026

Abstract

Connected vehicle platforms enable large-scale collection of vehicle dynamics data from production fleets, creating opportunities for passive roadway monitoring using onboard sensing systems. While existing vibration-based approaches primarily focus on pavement roughness estimation, the ability of fused onboard signals to capture defect-level characteristics [...] Read more.

Connected vehicle platforms enable large-scale collection of vehicle dynamics data from production fleets, creating opportunities for passive roadway monitoring using onboard sensing systems. While existing vibration-based approaches primarily focus on pavement roughness estimation, the ability of fused onboard signals to capture defect-level characteristics remains insufficiently explored. This study investigates whether Road Surface Monitoring (RSM) signals, developed by Honda as an integrated OEM sensing approach, contain distinguishable patterns associated with specific road surface defects. A framework is developed to analyze, detect, and classify defect-related vibration signatures using these fused signals. The approach introduces the Defect Consistency Index (DCI), which measured a 29% average difference between pothole and patching signal signatures within the dataset. A threshold-based Defect Identification Algorithm (DIA) was then applied to detect defective segments, achieving 89% detection accuracy. A machine learning pipeline using shape-based features was subsequently used to classify potholes and patching, achieving up to 90% classification accuracy on the evaluated dataset. The framework was evaluated using real-world RSM data collected from a single instrumented vehicle within a limited geographic region. The results indicate that fused vibration signals contain recurring defect-related patterns that may support defect-level analysis using compact, non-visual measurements. These findings indicate the potential of connected vehicle vibration sensing for scalable roadway monitoring while highlighting the need for broader validation across vehicles, environments, and defect conditions. Full article

(This article belongs to the Special Issue Remote Sensing for Infrastructure Health Monitoring: Advancements in Sensors and Analysis)

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20 pages, 19830 KB

Open AccessArticle

Sustainable Surface Treatments Using Dry-Process Rubber-Modified Asphalt in Cold Regions: A Laboratory, Field, and LCA Study

by Sepehr Mohammadi, Dongzhao Jin, Meng Wu, Zhongda Liu and Zhanping You

Infrastructures 2026, 11(6), 199; https://doi.org/10.3390/infrastructures11060199 - 11 Jun 2026

Abstract

The incorporation of crumb rubber derived from waste tires in asphalt pavements has gained increasing attention as a strategy to enhance performance while reducing environmental impacts, particularly in cold regions such as the Midwestern United States, where pavements are subjected to severe thermal [...] Read more.

The incorporation of crumb rubber derived from waste tires in asphalt pavements has gained increasing attention as a strategy to enhance performance while reducing environmental impacts, particularly in cold regions such as the Midwestern United States, where pavements are subjected to severe thermal stresses and freeze–thaw cycles. Despite the numerous performance benefits observed in previous laboratory-scale studies, field demonstrations can play a critical role in validating the use of recycled waste tires as asphalt additives. This study examines the performance benefits and environmental impacts of incorporating recycled tire rubber into asphalt mixtures via a dry modification process for cold-climate applications. Building on these findings, this paper is based on a full-scale field demonstration of a dry-process rubber-modified asphalt pavement constructed in Ann Arbor, Michigan. Performance testing was conducted at both the binder and mixture levels, and field cores were collected during the construction of field sections. To complement the performance evaluation, a life-cycle assessment (LCA) was conducted to quantify the environmental impacts of rubber-modified asphalt and conventional asphalt. The results indicate that successful rubber incorporation, combined with improved low-, intermediate-, and high-temperature performance, enhances long-term durability compared with control sections. Moreover, despite slightly higher initial environmental impacts associated with rubber incorporation, improved durability and reduced maintenance frequency can lead to lower life-cycle impacts over the long term. The findings highlight the potential of rubber-modified asphalt as a sustainable, resilient solution for cold-region pavements, offering practical insights for agencies seeking to balance performance and environmental impacts in future infrastructure design. Full article

(This article belongs to the Special Issue Sustainable Materials and Design for Wearing Courses and Surface Treatments)

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14 pages, 8925 KB

Open AccessArticle

Performance Evaluation of High-RAP Asphalt Mixtures Incorporating Rejuvenators, Regenerators, and Softer Binders

by David López-García, Carlos Alonso-Troyano and David Llopis-Castelló

Infrastructures 2026, 11(6), 198; https://doi.org/10.3390/infrastructures11060198 - 10 Jun 2026

Abstract

The need for potentially more sustainable road rehabilitation solutions has driven the use of reclaimed asphalt pavement (RAP) in bituminous mixtures. However, high-RAP content remains a technical challenge due to binder ageing, which increases mixture stiffness and adversely affects its mechanical performance. The [...] Read more.

The need for potentially more sustainable road rehabilitation solutions has driven the use of reclaimed asphalt pavement (RAP) in bituminous mixtures. However, high-RAP content remains a technical challenge due to binder ageing, which increases mixture stiffness and adversely affects its mechanical performance. The aim of this research is to evaluate three strategies for correcting aged binder in asphalt concrete (AC) 16 surf S mixtures containing 50% RAP: rejuvenator, regenerator, and softer virgin bitumen. To this end, four asphalt mixtures were evaluated through tests on air void content, water sensitivity, resistance to permanent deformation, and stiffness modulus, in accordance with European standards. The results show that the reference mixture without binder correction exhibits excessive stiffness, whereas the mixture incorporating a rejuvenator showed the most favorable combination of the mechanical indicators evaluated, combining a significant reduction in stiffness modulus with high water resistance and adequate rutting resistance. The mixture with regenerator showed an intermediate response, while the exclusive use of a softer bitumen did not achieve satisfactory overall performance. The results confirm that the use of high-RAP contents in AC 16 surf S mixtures can be feasible, provided that an appropriate strategy for rheological correction of the aged binder is applied. Full article

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

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20 pages, 7971 KB

Open AccessArticle

Data Cleansing for Robust Modal Parameter Tracking in Vibration-Based Structural Health Monitoring

by Carlo Rainieri, Santiago Gómez Molina, Ilenia Rosati and Alessio De Corso

Infrastructures 2026, 11(6), 197; https://doi.org/10.3390/infrastructures11060197 - 10 Jun 2026

Abstract

Vibration-based Structural Health Monitoring (SHM) exploits automated Operational Modal Analysis (OMA) to track changes in modal parameters over time for subsequent statistical pattern recognition and anomaly detection. However, weak excitation, measurement noise, non-stationarities, non-linearities, and model inaccuracies can jeopardize the reliability of automated [...] Read more.

Vibration-based Structural Health Monitoring (SHM) exploits automated Operational Modal Analysis (OMA) to track changes in modal parameters over time for subsequent statistical pattern recognition and anomaly detection. However, weak excitation, measurement noise, non-stationarities, non-linearities, and model inaccuracies can jeopardize the reliability of automated OMA and pollute the modal parameter time series with a number of outliers or spurious estimates. These have an impact on statistical pattern recognition and consequently, the anomaly detection accuracy. Thus, a preliminary data cleansing to enhance the robustness of modal parameter tracking is imperative to ensure the reliability of SHM outcomes. Clustering techniques represent an attractive solution to automatically identify underlying data patterns and discriminate possible spurious results. However, the curse of dimensionality is often an issue in the application of such techniques to time series of experimentally identified modal parameters. To mitigate this issue and, at the same time, the computational efforts, the present study proposes an innovative approach leveraging clustering techniques coupled with mode-pairing constraints for robust and automatic tracking of modal parameters in the context of vibration-based SHM applications. Different clustering algorithms have been embedded in the proposed data processing strategy and applied to a real dataset collected on a full-scale structure under operational conditions. The comparative performance assessment demonstrated how DBSCAN outperforms other clustering methods in the context of the proposed approach, allowing the effective separation of the physical poles from the spurious ones even in the presence of closely spaced modes and highly polluted feature space. Full article

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21 pages, 3842 KB

Open AccessArticle

Analysis of Tension Piles Supporting Large Structures Using Parabolic Soil Model and Elastic–Perfectly Plastic Pile Material

by Sudip Basack, Meshel Q. Altahtani, Saiful Islam and Moses Karakouzian

Infrastructures 2026, 11(6), 196; https://doi.org/10.3390/infrastructures11060196 - 9 Jun 2026

Abstract

Large civil infrastructures, including high-rise buildings, bridges, offshore platforms, transmission towers, tall chimneys, basements below the water table, etc., are often supported on pile foundations. Apart from the usual dead loads and live loads imposed by superstructures, these piles are often subjected to [...] Read more.

Large civil infrastructures, including high-rise buildings, bridges, offshore platforms, transmission towers, tall chimneys, basements below the water table, etc., are often supported on pile foundations. Apart from the usual dead loads and live loads imposed by superstructures, these piles are often subjected to significant uplift forces due to overturning moments or hydrostatic pressure resulting from the effects of wind and wave loading, traffic movement, buoyancy, etc. Piles that withstand tensile loads are termed tension piles. Since the soil is unable to resist tensile stress, the pullout loads imposed on tension piles are prevented primarily by downward skin friction at the pile–soil interface, as well as by the self-weight of the piles. In this paper, a numerical model was developed using boundary element analysis, wherein piles were assumed to be made of an elastic–perfectly plastic material, and the soil was modeled using a parabolic model. The developed model was validated with available experimental results, and acceptable agreement was found. An in-depth study by detailed parametric analysis revealed that the parabolic soil model yielded satisfactory results. Extensive full-scale case studies were also performed to study the influence of various factors on tension pile performance. A set of important conclusions was drawn from the entire work. Full article

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30 pages, 50342 KB

Open AccessArticle

A Vision Zero-Oriented Diagnostic Framework for Complex Junctions Using UAV-Based Trajectory Analysis

by Laura Valentina Hernández García, Irene Méndez-Manjón, Eva Martínez López and Pedro Plasencia-Lozano

Infrastructures 2026, 11(6), 195; https://doi.org/10.3390/infrastructures11060195 - 8 Jun 2026

Abstract

This study presents a replicable diagnostic framework for analysing latent safety vulnerability at complex junctions by integrating UAV-based observation, trajectory extraction, movement-level operational performance modelling, and regulatory benchmarking. Grounded in Vision Zero/Safe System principles, the approach is demonstrated at Junction 50 of the [...] Read more.

This study presents a replicable diagnostic framework for analysing latent safety vulnerability at complex junctions by integrating UAV-based observation, trajectory extraction, movement-level operational performance modelling, and regulatory benchmarking. Grounded in Vision Zero/Safe System principles, the approach is demonstrated at Junction 50 of the A-66 motorway in Mieres (Spain), a constrained urban interchange where motorway access/egress overlaps with local cross-town movements. Two one-hour UAV datasets were collected during peak periods and processed with GoodVision to extract trajectories, turning-movement counts, origin–destination patterns and recurrent irregular manoeuvres. These outputs were combined with SIDRA INTERSECTION indicators (e.g., LOS, delay and capacity utilisation) and assessed against the Spanish geometric design standard to identify design–operation misalignment, including a targeted 3D sight-distance check at the stop-controlled exit. The results show systematic behavioural adaptations at critical decision points, including informal side-by-side queuing at nominally single-lane exits and queue bypassing via adjacent auxiliary areas, co-occurring with capacity-strained movements (LOS E–F) and normative inconsistencies in lane type/length and channelisation. The framework translates high-resolution behavioural evidence into intervention-relevant outputs (critical movements, concentration zones and explicit design–behaviour mismatches) without relying on crash frequency as the primary signal, supporting proactive prioritisation in constrained legacy layouts. Full article

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

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23 pages, 4540 KB

Open AccessArticle

Torque-Based Evaluation and Predictive Modeling of Asphalt Mixture Workability Using a High-Capacity Mixing Device

by Hawraa F. Jabbar, Miami M. Hilal, Mohammed Y. Fattah, Karim Sherif Mostafa, Norbaya Sidek and Mohamed A. Hafez

Infrastructures 2026, 11(6), 194; https://doi.org/10.3390/infrastructures11060194 - 8 Jun 2026

Abstract

This study investigates asphalt mixture workability using a high-capacity torque-based device under semi-industrial laboratory conditions. Unlike conventional laboratory-scale mixers, the proposed system accommodates batch sizes up to 15 kg, enabling more realistic simulation of plant and field mixing conditions. Torque response was monitored [...] Read more.

This study investigates asphalt mixture workability using a high-capacity torque-based device under semi-industrial laboratory conditions. Unlike conventional laboratory-scale mixers, the proposed system accommodates batch sizes up to 15 kg, enabling more realistic simulation of plant and field mixing conditions. Torque response was monitored during the mixing of conventional, warm-mix, and RAP-containing asphalt mixtures. Predictive models were developed using stepwise regression to relate torque to mixture parameters, including temperature, RAP content, and binder type. Results indicate that RAP significantly increases mixing torque, while elevated temperatures reduce resistance to mixing. Although the developed models demonstrated moderate to good explanatory power (R² = 0.63–0.77), they provide useful comparative insights into asphalt mixture workability rather than absolute predictions. The proposed torque-based methodology offers a practical framework for workability assessment and quality control of asphalt mixtures beyond traditional laboratory scales. Full article

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23 pages, 12799 KB

Open AccessArticle

Study on the Rheological Properties and Composition of SBS-Modified Bitumen in Xinjiang Under Short-Term Thermal-Oxidative and Long-Term Oxidative Pressure Aging

by Yingchun Yin, Wengui Zhang, Wei Wan, Yile Chen and Zunqing Liu

Infrastructures 2026, 11(6), 193; https://doi.org/10.3390/infrastructures11060193 - 7 Jun 2026

Abstract

To investigate the rheological properties and compositional changes in SBS-modified bitumen under different aging conditions in the unique environmental conditions of the Xinjiang region, this study selected a local 70# base bitumen from Xinjiang and prepared modified bitumen by adding 4.0%, 4.5%, and [...] Read more.

To investigate the rheological properties and compositional changes in SBS-modified bitumen under different aging conditions in the unique environmental conditions of the Xinjiang region, this study selected a local 70# base bitumen from Xinjiang and prepared modified bitumen by adding 4.0%, 4.5%, and 5.0% SBS modifier, respectively. RTFOT and PAV were used to simulate the short-term thermal-oxidative aging and long-term oxidative pressure aging processes of the bitumen samples, respectively. The three key indicators and dynamic rheological properties of the bitumen were tested for the original sample, as well as before and after short-term thermal-oxidative aging and long-term oxidative pressure aging. Thin-layer chromatography/flame ionization detection (TLC/FID) was used to analyze the migration patterns of the samples’ chemical components, and a random forest model was employed to establish a quantitative mapping between the four components of the modified bitumen and the rutting factor over a wide temperature range. The results indicate that aging weakens the improvement effect of SBS on the high-temperature performance of bitumen. However, 4.5% SBS-modified bitumen subjected to long-term oxidative pressure aging still maintains the best high- and low-temperature performance, elastic recovery capacity, and fatigue resistance compared to other dosage levels. It also has the highest bitumen content, which verifies the high-temperature performance of this dosage at the component level. Therefore, the optimal SBS dosage is recommended to be 4.5%. Notably, as the SBS content increases, it significantly regulates the increase in heavy fraction content during the aging process, while the decrease in light fraction content is not significantly affected by the content. Based on the random forest algorithm, a mapping relationship between fractions and properties under fully aged conditions was established. This study provides a theoretical basis for research on the modification and aging mechanisms of Xinjiang bitumen. Full article

(This article belongs to the Special Issue Advances in Pavement Engineering: Materials, Performance, and Sustainability)

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23 pages, 9796 KB

Open AccessArticle

Application of Low-Cost Remote Sensors to Capture Displacements with Sub-mm Tracking Precision

by Anna M. Rakoczy, Joanna Szczech and Jan Winkler

Infrastructures 2026, 11(6), 192; https://doi.org/10.3390/infrastructures11060192 - 5 Jun 2026

Abstract

Regulations in Poland require acceptance load tests to verify bridge response under moving loads before structures are approved for operation. These tests are mandatory for new bridges, after major renovations, and for reconstructed structures, and may also be conducted as supplementary assessments of [...] Read more.

Regulations in Poland require acceptance load tests to verify bridge response under moving loads before structures are approved for operation. These tests are mandatory for new bridges, after major renovations, and for reconstructed structures, and may also be conducted as supplementary assessments of existing bridges to determine their load-carrying capacity. This paper presents one of the first documented applications, to the authors’ knowledge, of low-cost sensing technology for capturing bridge displacements with sub-millimeter tracking precision during acceptance load testing. The study explores the use of modern remote sensing methods based on digital image correlation (DIC) to assess vertical displacements of a truss railway bridge span under moving loads. Video data were recorded using a standard smartphone under nighttime conditions with artificial lighting, demonstrating a highly accessible and cost-effective measurement approach. The collected data were processed using the DES Vision System and compared with results obtained from traditional measurement techniques, such as accelerometers, enabling an evaluation of the accuracy and precision of the DIC method. The findings show that smartphone-based video recordings can provide displacement measurements with millimeter- to sub-millimeter-level tracking precision. Additionally, a numerical finite element method (FEM) model was developed to support interpretation of the structural response under moving loads. Full article

(This article belongs to the Special Issue Remote Sensing for Infrastructure Health Monitoring: Advancements in Sensors and Analysis)

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23 pages, 7688 KB

Open AccessArticle

Experimental Study of a Composite Modifying Additive Based on Industrial By-Products for Enhancing Durability of Portland Cement Concrete

by Adiya Zhumagulova, Rauan Lukpanov, Duman Dyussembinov, Mariya Smagulova, Galiya Asanova, Manarbek Zhumamuratov, Andrey Chzhen and Daniyar Zakirzhan

Infrastructures 2026, 11(6), 191; https://doi.org/10.3390/infrastructures11060191 - 5 Jun 2026

Abstract

This article presents the results of tests evaluating the physical and mechanical properties of a modified hydraulic concrete formulation based on Portland cement, intended for use in general construction. The additive consists of post-alcohol distiller’s grains (PaB), soapstock (Sp), caustic soda (NaOH), granite [...] Read more.

This article presents the results of tests evaluating the physical and mechanical properties of a modified hydraulic concrete formulation based on Portland cement, intended for use in general construction. The additive consists of post-alcohol distiller’s grains (PaB), soapstock (Sp), caustic soda (NaOH), granite dust (Gr) and acrylic latex (Lx). These components contribute to transforming the strength characteristics of concrete in compression and bending, as well as its water absorption, water permeability and chemical resistance. Based on the results obtained, the effectiveness of the additive was assessed, as was the quantitative improvement in concrete properties, including an evaluation of the life cycle of reinforced concrete structures in aggressive environments. According to the research results, an optimal composition was obtained which increases compressive strength by 6.2%, flexural strength by 7.9%, decreases water absorption by 50.1%, decreases the filtration coefficient by 97.4%, and increases chemical resistance by 42.8%. Full article

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30 pages, 7879 KB

Open AccessArticle

Machine Learning for Relative Compressive Strength of Concrete Incorporating Agricultural Bio-Supplementary Cementitious Materials

by Leila Mirzaei, Clifford B. Fedler and Tewodros Ghebrab

Infrastructures 2026, 11(6), 190; https://doi.org/10.3390/infrastructures11060190 - 5 Jun 2026

Abstract

Agricultural biomass ashes are increasingly used as sustainable supplementary cementitious materials (SCMs) to reduce cement-related carbon emissions and improve concrete performance. However, their effects on compressive strength depend on the SCM type, replacement level, and physical and chemical properties. These variables are often [...] Read more.

Agricultural biomass ashes are increasingly used as sustainable supplementary cementitious materials (SCMs) to reduce cement-related carbon emissions and improve concrete performance. However, their effects on compressive strength depend on the SCM type, replacement level, and physical and chemical properties. These variables are often overlooked in machine learning studies focused on single SCM types and absolute strength prediction, limiting transferability across heterogeneous SCM datasets. This study develops an interpretable machine learning framework using a compiled dataset covering 18 agricultural biomass ash SCMs (bio-SCMs) used in concrete. Input features include concrete mixture proportions, the SCM replacement level, chemical composition, and specific surface area (SSA), while the target variable is the 28-day compressive-strength ratio relative to the companion control mixture. Among the five evaluated models, XGBoost achieved the best performance, with weighted 10-fold cross-validation R² values around 0.80. SHapley Additive exPlanations (SHAP) results were interpreted as model associations rather than causal mechanisms. Higher SCM SiO₂ content, pozzolanic oxide content, superplasticizer dosage, and baseline control mixture strength were associated with more favorable strength ratios; SCM SSA showed a mild positive tendency, whereas a higher SCM replacement level, water-to-binder ratio, and loss on ignition were associated with less favorable strength ratios. SCM-specific response analysis further identified literature-derived screening ranges based on observed and interpolated replacement levels rather than machine learning extrapolation. Full article

(This article belongs to the Special Issue AI in Sustainable and Resilient Infrastructures: Construction, Management, and Maintenance)

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20 pages, 29898 KB

Open AccessArticle

Automated Pre-Processing and BIM Implementation of Half-Cell Potential Measurements

by Domenic Graffi, Hendrik Morgenstern and Katharina Klemt-Albert

Infrastructures 2026, 11(6), 189; https://doi.org/10.3390/infrastructures11060189 - 4 Jun 2026

Abstract

Analog, paper-based workflows remain the norm in the condition assessment of reinforced concrete infrastructure, limiting the efficiency with which diagnostic data can be used in maintenance planning. In this research, a pre-processing procedure and BIM implementation workflow for half-cell potential measurement data are [...] Read more.

Analog, paper-based workflows remain the norm in the condition assessment of reinforced concrete infrastructure, limiting the efficiency with which diagnostic data can be used in maintenance planning. In this research, a pre-processing procedure and BIM implementation workflow for half-cell potential measurement data are proposed, implemented in open-source software, to enable data-efficient integration of diagnostic information into a BIM model. By interpreting the potential mappings as point clouds, areas relevant for maintenance planning are automatically identified through the analysis of geometric features. These identified areas are subsequently transformed into BIM objects that carry the relevant diagnostic information. The results demonstrate that the pre-processing procedure and BIM implementation workflow reduce the number of required BIM objects by over 98% (from 27,040 to 434 elements) and the IFC file size by 97% (from 77.8 MB to 2.3 MB), enabling a lightweight BIM implementation with substantially improved rendering performance. Full article

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

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25 pages, 9169 KB

Open AccessArticle

Hyperbola Occurrence in GPR Radargrams of Cracked Road Pavements: A Numerical Comparison of Top-Down and Bottom-Up Cracking

by Grigório Neto, Jorge Pais, Simona Fontul and Francisco Fernandes

Infrastructures 2026, 11(6), 188; https://doi.org/10.3390/infrastructures11060188 - 3 Jun 2026

Abstract

Ground-penetrating radar is widely used in non-destructive pavement evaluation, but the occurrence of multiple hyperbolic signatures in radargrams of cracked pavements remains insufficiently characterized, particularly for top-down and bottom-up cracking. This study investigates the occurrence of detectable hyperbolas in numerical GPR radargrams by [...] Read more.

Ground-penetrating radar is widely used in non-destructive pavement evaluation, but the occurrence of multiple hyperbolic signatures in radargrams of cracked pavements remains insufficiently characterized, particularly for top-down and bottom-up cracking. This study investigates the occurrence of detectable hyperbolas in numerical GPR radargrams by comparing two crack models under a controlled two-dimensional numerical design. Model A represents top-down cracking, and Model B represents bottom-up cracking. For each model, four parametric studies were performed by varying crack width, crack depth, asphalt-layer thickness, and granular-layer thickness, yielding 32 simulations in total. All cases were modeled in gprMax2D at 2300 MHz and processed in MATLAB through radargram pre-processing, central A-scan candidate detection, lateral tracking of hyperbolic events, and final classification based on stable retained trajectories. Model A was predominantly characterized by 3H responses, whereas Model B was predominantly characterized by 2H responses, with no 3H case observed. In Model A, crack-width increase was associated with the strongest occurrence change, whereas in Model B, greater asphalt-layer thickness was associated with a reduction from 2H to 1H. The first apex TWT provided a complementary discriminator between the two models. These findings provide controlled numerical reference trends that may support the interpretation of hyperbola occurrence in GPR-based pavement crack assessment. Full article

(This article belongs to the Special Issue Advanced Technologies for Civil Infrastructure Monitoring)

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32 pages, 6586 KB

Open AccessArticle

Seismic Torsional Behavior of Step-Terrace Mountain Isolated Structures with Isolation-Layer Eccentricity: Shaking Table Tests

by Zhanjing Wu, Zhong Tao, Longfei Zhang, Zhengjia Wu, Qiang Huang and Haisu Sun

Infrastructures 2026, 11(6), 187; https://doi.org/10.3390/infrastructures11060187 - 1 Jun 2026

Abstract

To investigate the influence of isolation-layer eccentricity on the torsional response of step-terrace mountain (STM) structures, a 1:10 scaled reinforced concrete model was designed and tested using shaking table experiments. Both isolated and non-isolated configurations were considered, and different eccentricity levels were achieved [...] Read more.

To investigate the influence of isolation-layer eccentricity on the torsional response of step-terrace mountain (STM) structures, a 1:10 scaled reinforced concrete model was designed and tested using shaking table experiments. Both isolated and non-isolated configurations were considered, and different eccentricity levels were achieved by adjusting the bearing layouts in the upper and lower isolation layers. The torsional response was evaluated in terms of torsional angle, torsional displacement ratio, and relative torsional effect. The results indicate that the non-isolated STM structure exhibits pronounced torsional amplification and progressive damage accumulation. Deformation and damage are concentrated in the upper stories and dropped-story region, eventually leading to a stiffness–degradation–dominated failure pattern. In contrast, the STM isolated structure effectively suppresses torsional response, and inter-story rotations remain small and relatively uniform along the height, indicating that seismic deformation is primarily redistributed within the isolation layers rather than amplified in the superstructure. The experimental results further demonstrate that torsional behavior is governed by the coupling effect between isolation-layer eccentricity and seismic input direction. The eccentricity in the upper isolation layer plays the dominant role in triggering torsional amplification, while simultaneous eccentricities in both isolation layers produce a cumulative torsional effect. When the eccentricity of the isolation layers is controlled within 5%, the torsional displacement ratio remains below 1.2, while the non-isolated structure reaches values exceeding the code limit of 1.5. In addition, slope-direction excitation intensifies absolute torsional deformation due to overturning effects induced by elevation differences. These findings highlight that torsional response in STM isolated systems is controlled by the interaction between vertical irregularity and isolation-system asymmetry. Full article

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

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23 pages, 2245 KB

Open AccessArticle

Laboratory Evaluation of Asphalt Mixtures Reinforced with Corn Husk Fiber Powder

by Abbas F. Jasim, Rana A. Yousif, Sady A. Tayh, Safaa A. Mohamad and Teba T. Khaled

Infrastructures 2026, 11(6), 186; https://doi.org/10.3390/infrastructures11060186 - 28 May 2026

Abstract

The pavement surface temperatures in Iraq are remarkably high, causing the asphalt to deteriorate quickly, shortening its service life. While a large amount of corn husk, an agricultural waste, is available for use as an asphalt modifier, researchers have not yet fully investigated [...] Read more.

The pavement surface temperatures in Iraq are remarkably high, causing the asphalt to deteriorate quickly, shortening its service life. While a large amount of corn husk, an agricultural waste, is available for use as an asphalt modifier, researchers have not yet fully investigated this option. In this study, the use of corn husk fiber powder (CHFP) as a long-term modifier for asphalt binders and mixtures that are exposed to high-temperature conditions is evaluated. CHFP was mixed into a 40–50 penetration grade asphalt binder at concentrations ranging from 0.0% to 0.6% by weight. Performance was assessed using laboratory tests such as penetration, softening point, rotating viscosity, dynamic shear rheometer (DSR), aging (RTFOT and PAV), and wheel tracking. The findings revealed that CHFP greatly lowers penetration while increasing the softening point, indicating increased stiffness and high-temperature stability. Rheological research showed an increase in the rutting parameter (G*/sinδ) and viscosity, as well as reduced temperature susceptibility. At the mixed level, CHFP reduced rut depth while improving dynamic stability, indicating increased resistance to permanent deformation. The best performance was obtained at 0.3% CHFP, after which, improvements decreased due to probable dispersion constraints. The performance improvement is related to the creation of a reinforcing fiber network and the absorption of light asphalt components. Overall, CHFP is a promising, environmentally friendly and cost-effective addition for increasing asphalt pavement performance and promoting sustainable waste management. Full article

(This article belongs to the Section Sustainable Infrastructures)

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18 pages, 4382 KB

Open AccessArticle

Performance and Microstructural Characteristics of Ultra-Early High-Strength Cement-Based Grouting Materials Modified with Accelerating and Retarding Agents

by Xing-Ze Duan, Zhao-Jun Liu, Shuai-Qi Wang, Rui-Jie Xia, Wei Li, Ju Liu, Guo-Hua Song, Zhi-Xiao Shi, Jun Shi, Ao Yang and Kuang-Yu Dai

Infrastructures 2026, 11(6), 185; https://doi.org/10.3390/infrastructures11060185 - 26 May 2026

Abstract

To balance ultra-early strength development and workable time in cement-based grouting materials for rapid repair applications, an ultra-early high-strength grout system was developed by regulating the dosage of an accelerating agent (CF), retarder content, and water-to-binder ratio (w/b). The effects of these parameters [...] Read more.

To balance ultra-early strength development and workable time in cement-based grouting materials for rapid repair applications, an ultra-early high-strength grout system was developed by regulating the dosage of an accelerating agent (CF), retarder content, and water-to-binder ratio (w/b). The effects of these parameters on setting behavior, workability, mechanical properties, volumetric stability, and durability were systematically investigated. X-ray diffraction (XRD) and scanning electron microscopy coupled with energy-dispersive spectroscopy (SEM/EDS) were further conducted to qualitatively evaluate the hydration characteristics and microstructural evolution of the optimized system. The results showed that CF accelerated early hydration and promoted the rapid formation of ettringite (AFt), which contributed to the development of ultra-early strength. The incorporation of a retarder effectively prolonged the workable time and improved slurry workability. Increasing the w/b ratio enhanced flowability and toughness, although excessive w/b reduced compressive strength. The optimal mixture contained 30% CF, 0.02% retarder, and a w/b ratio of 0.19. Under this condition, the grout exhibited a flowability of 312 mm and compressive strengths of 81.4 MPa at 1 h and 121.3 MPa at 28 d. In addition, low air shrinkage (0.027% at 28 d) and excellent chloride penetration resistance (12 C at 28 d) were achieved. Microstructural observations suggested that the dense structure formed by AFt and C–S–H gel contributed to the improved macroscopic performance. This study provides an engineering-oriented reference for the mix design and performance optimization of ultra-early high-strength cement-based grouting materials for rapid repair applications. Full article

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23 pages, 4359 KB

Open AccessArticle

Machine Learning-Assisted Multi-Objective Optimization of Surface Pretreated Coal Gangue Lightweight Shotcrete

by Wencan Huang, Wei Huang, Wenjia Huang, Qingxiang Zhao, Lingyu Zhong, Wendi Deng, Yufei Wang, Qianqian Dong, Jianxiong Liao and Cai Min

Infrastructures 2026, 11(6), 184; https://doi.org/10.3390/infrastructures11060184 - 25 May 2026

Abstract

The large-scale accumulation of coal gangue has created increasing environmental pressure, while its use as aggregate in cementitious materials remains limited by its high water absorption, porous structure and unstable mechanical performance. This study develops a machine learning-assisted multi-objective optimization framework for lightweight [...] Read more.

The large-scale accumulation of coal gangue has created increasing environmental pressure, while its use as aggregate in cementitious materials remains limited by its high water absorption, porous structure and unstable mechanical performance. This study develops a machine learning-assisted multi-objective optimization framework for lightweight shotcrete incorporating surface-pretreated coal gangue aggregates and polyvinyl alcohol fibres. Two pretreatment methods—namely, silica-fume slurry coating (CGACM) and dry adsorption activation (CGACD)—were applied to improve the aggregate surface characteristics. Experimental data on compressive strength, splitting strength and density were used to train backpropagation neural networks and support vector machine and random forest models, with hyperparameters optimized by the Beetle Antennae Search algorithm. The trained models were then coupled with a multi-objective optimization procedure to balance mechanical performance, density, material cost and CO₂ emissions. The results show that surface pretreatment can improve the performance of coal gangue lightweight shotcrete, while the proposed optimization framework can identify mixture designs with balanced strength, reduced density and improved economic and environmental performance. Compared with untreated or non-optimized mixtures, the optimized surface-pretreated mixtures achieved a more favorable trade-off among mechanical, cost and carbon-emission objectives. This study provides a data-driven approach for the sustainable design and practical utilization of coal gangue in lightweight shotcrete. Full article

(This article belongs to the Special Issue Construction and Maintenance of Transportation Infrastructure in Extreme Environments)

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