Infrastructures

25 pages, 4439 KB

Open AccessArticle

Bridging Gaps in Landslide Mapping: A Semi-Quantitative Empirical Framework for Delineating Key Areas to Improve Collection of Essential Field-Based and Supplementary Remote-Based Data

by Nicola Perilli, Massimiliano Lombardi, Nunziante Squeglia, Stefano Stacul and Stefano Pagliara

Infrastructures 2026, 11(1), 11; https://doi.org/10.3390/infrastructures11010011 - 25 Dec 2025

Abstract

Accurate landslide mapping near critical infrastructure requires not only data on landslide characteristics but also clear definitions of the spatial extent of surveyed areas. While national projects like Italian Landslide Inventory (IFFI) and Italian Guidelines for the classification and management of risk, safety [...] Read more.

Accurate landslide mapping near critical infrastructure requires not only data on landslide characteristics but also clear definitions of the spatial extent of surveyed areas. While national projects like Italian Landslide Inventory (IFFI) and Italian Guidelines for the classification and management of risk, safety assessment and monitoring of existing bridges (LLG 2022) provide a list of data to collect during a field visit survey, they lack clear specifications for buffer zones, limiting data comparability and risk assessment reliability. This study refines a hierarchical framework developed by the FABRE Geo Working Group, in alignment with LLG 2022, introducing five key zones—Landslide Inventory Reference Area, Diagnostic Area, Geomorphological Significant Area, Relevant Area and the Approach Zone, plus a newly defined Geomorphological Significant Area—Close Zone. By explicitly quantifying buffer zones and their hierarchical roles, the framework ensures consistent data collection across varied terrains and reduces ambiguity in landslide risk evaluation. Applied to 95 bridges in Tuscany and Basilicata, the framework offers standardized definitions and dimensions for Diagnostic Area, Geomorphological Significant Area and Relevant Area, based on detailed field surveys. Approach Zone and Geomorphological Significant Area—Close Zone are quantified as percentages of Relevant Area and Geomorphological Significant Area, supporting efficient, reproducible inspections using both manual and UAV-assisted methods. The Geomorphological Significant Area—Close Zone distinguishes core data, which requires direct surveys, from supplementary data that can be analyzed remotely or in the office. This distinction ensures that essential hazards are observed directly, while supplementary insights are efficiently integrated, enhancing field reliability and desk-based analysis. This integrated approach enhances the accuracy of landslide susceptibility assessment and the classification of attention levels, supporting the maintenance of the national IFFI. Ultimately, the comparison of IFFI catalog data, available in the Diagnostic Area, Geomorphological Significant Area, and Relevant Area, revealed previously unrecorded landslides in Matera and confirmed the reliability of the catalog in Lucca, highlighting that inventories can be systematically integrated only by using standardized areas with field verification to improve risk and infrastructure management. The structured framework bridges gaps between national inventory standards and localized survey needs, ensuring that both previously recorded and new landslide events are systematically captured. Full article

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

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32 pages, 6741 KB

Open AccessArticle

Coupled ALE–Lagrangian Analysis of Pavement Damage Induced by Buried Natural Gas Pipeline Explosions

by Lijun Li, Jianying Chen, Jiguan Liang and Zhengshou Lai

Infrastructures 2026, 11(1), 10; https://doi.org/10.3390/infrastructures11010010 - 24 Dec 2025

Abstract

This study numerically investigates pavement damage caused by explosions in buried leaking natural gas pipelines using a coupled Lagrangian–Eulerian (CLE) framework in LS-DYNA. The gas phase is described by a Jones–Wilkins–Lee-based equation of state, while soil and pavement are modeled using a pressure-dependent [...] Read more.

This study numerically investigates pavement damage caused by explosions in buried leaking natural gas pipelines using a coupled Lagrangian–Eulerian (CLE) framework in LS-DYNA. The gas phase is described by a Jones–Wilkins–Lee-based equation of state, while soil and pavement are modeled using a pressure-dependent soil model and the Riedel–Hiermaier–Thoma concrete model with strain-based erosion, respectively. The approach is validated against benchmark underground explosion tests in sand and blast tests on reinforced concrete slabs, demonstrating accurate prediction of pressure histories, ejecta evolution, and crater or damage patterns. Parametric analyses are then conducted for different leaked gas masses and pipeline burial depths to quantify shock transmission, soil heave, pavement deflection, and damage evolution. The results indicate that the dynamic response of the pavement structure is most pronounced directly above the detonation point and intensifies significantly with increasing total leaked gas mass. For a total leaked gas mass of 36 kg, the maximum vertical deflection, the peak kinetic energy, and the peak pressure at the bottom interface at this location reach 148.46 mm, 14.64 kJ, and 10.82 MPa, respectively. Moreover, a deflection-based index is introduced to classify pavement response into slight (<20 mm), moderate (20–40 mm), severe (40–80 mm), and collapse (>80 mm) states, and empirical curves are derived to predict damage level from leakage mass and burial depth. Finally, the effectiveness of carbon fiber reinforced polymer (CFRP) strengthening schemes is assessed, showing that top and bottom surface reinforcement with a total CFRP thickness of 2.67 mm could reduce vertical deflection by up to 37.93% and significantly mitigates longitudinal cracking. The results provide a rational basis for safety assessment and blast resistant design of pavement structures above buried gas pipelines. Full article

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

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24 pages, 6733 KB

Open AccessArticle

Prediction of Concrete Arch Dam Response Using Locally Estimated Scatterplot Smoothing

by Narjes Soltani, Ignacio Escuder-Bueno and David Galán

Infrastructures 2026, 11(1), 9; https://doi.org/10.3390/infrastructures11010009 - 23 Dec 2025

Abstract

In this research, a novel hybrid methodology is proposed for predicting the structural response of high concrete arch dams, combining the Discrete Element Method (DEM) with the Locally Estimated Scatterplot Smoothing (LOESS) technique. A structured calibration strategy is employed during the numerical model [...] Read more.

In this research, a novel hybrid methodology is proposed for predicting the structural response of high concrete arch dams, combining the Discrete Element Method (DEM) with the Locally Estimated Scatterplot Smoothing (LOESS) technique. A structured calibration strategy is employed during the numerical model preparation to enable the generation of a wide range of reliable output variables for training and prediction. The methodology is then applied to the El Atazar arch dam to demonstrate its capability to forecast displacement and stress responses. The study reveals that using the current air temperature as an input variable is not adequate for representing the thermal behavior of the dam body; instead, the mean air temperature over a specified period yields significantly better results. Additionally, the findings highlight the importance of the loading path and the dam’s initial state in determining its structural response. The developed model shows a strong agreement between predicted and observed data, demonstrating its effectiveness in capturing the nonlinear behavior of high concrete arch dams. Compared to traditional parametric models commonly used for dam deformation analysis, the proposed framework offers greater flexibility in representing nonlinearity while requiring less training data, making it ideal for dams with limited monitoring records, such as older dams or newly operated ones. Full article

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

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18 pages, 1521 KB

Open AccessArticle

Visibility of Vertical Road Signs in Real Driving Environments: Effects of Retroreflectivity and Surface Conditions

by Claudia Brasile, Margherita Pazzini, Davide Chiola, Andrea Simone, Claudio Lantieri and Valeria Vignali

Infrastructures 2026, 11(1), 8; https://doi.org/10.3390/infrastructures11010008 - 23 Dec 2025

Abstract

The visibility of vertical road signs is a crucial factor for driving safety, especially in low-light conditions. The retroreflectivity of signs is imperative to ensure that drivers are able to perceive the information in a timely manner. However, the effectiveness of signs can [...] Read more.

The visibility of vertical road signs is a crucial factor for driving safety, especially in low-light conditions. The retroreflectivity of signs is imperative to ensure that drivers are able to perceive the information in a timely manner. However, the effectiveness of signs can be compromised by factors such as material degradation, wear and tear, and dirt on the surface. The objective of this study is to analyze how different surface conditions and different levels of retroreflectivity of vertical signs affect users’ perception and driving behavior in a real controlled environment. A total of twenty-five volunteers undertook the same road test twice. During the initial trial, the subjects encountered signs with a Class II retro-reflective film (EN 12899-1:2007), and during the second trial, they encountered the same signs in the same positions as the first trial but with varied characteristics and additional factors such as dirt, water, and degradation. Through a Mobile Eye Tracker and a Racelogic Video Vbox, it was possible to investigate the alterations in the visual and kinematic behavior of participants across the two tests. The statistical analysis was conducted using the Wilcoxon test, Spearman’s correlation and regression analysis. The analysis revealed that the signal with a dirty surface had the most significant impact on participants’ perception, showing a substantial reduction in the distance of the first fixation (−15%), a decrease in the number of fixations (−37%), and an increase in the time required for it to be perceived (+40%). This study demonstrates that the maintenance of road sign surfaces is a critical factor in their effectiveness and is as influential as the level of retroreflectivity of the material. Full article

(This article belongs to the Special Issue Sustainable Infrastructures for Urban Mobility, 2nd Edition)

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15 pages, 3396 KB

Open AccessArticle

Seismic Response Analysis of Multi-Span SFT with Flexible Constraints

by Jiang Chen, Mingyuan Ma, Dan Wang, Xing Chen, Yin Zheng and Yonggang Shen

Infrastructures 2026, 11(1), 7; https://doi.org/10.3390/infrastructures11010007 - 23 Dec 2025

Abstract

The boundary of a submerged floating tunnel (SFT) is flexible, and ignoring the influence of boundary and pipeline connections may reduce its structural performance. Therefore, this study uses rotating springs and linear springs to simulate the flexible boundary. Joints are simplified as shear [...] Read more.

The boundary of a submerged floating tunnel (SFT) is flexible, and ignoring the influence of boundary and pipeline connections may reduce its structural performance. Therefore, this study uses rotating springs and linear springs to simulate the flexible boundary. Joints are simplified as shear springs and bending springs. A multi-span SFT model on discrete elastic supports is established, and its seismic response is evaluated using the transfer matrix method and the modal superposition method. The proposed method is validated by comparing it with finite element results, and the vertical mechanical response of the SFT when the cable relaxes or fractures under earthquake action is analyzed. The results indicate a significant deviation between the seismic response of flexible constraints and those modeled as simple hinged or fixed connections, and the lower boundary constraint stiffness is beneficial to the seismic response of the SFT. Introducing flexible joints can effectively reduce the internal force response of the structure, and a bending stiffness ratio of 0.01 to 0.03 for the joints is considered reasonable. In contrast, variations in the shear stiffness of the joints have a relatively small impact on the seismic response. Full article

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15 pages, 1308 KB

Open AccessArticle

Evolution of Convolutional and Recurrent Artificial Neural Networks in the Context of BIM: Deep Insight and New Tool, Bimetria

by Andrzej Szymon Borkowski, Łukasz Kochański and Konrad Rukat

Infrastructures 2026, 11(1), 6; https://doi.org/10.3390/infrastructures11010006 - 22 Dec 2025

Abstract

This paper discusses the evolution of convolutional (CNN) and recurrent (RNN) artificial neural networks in applications for Building Information Modeling (BIM). The paper outlines the milestones reached in the last two decades. The article organizes the current state of knowledge and technology in [...] Read more.

This paper discusses the evolution of convolutional (CNN) and recurrent (RNN) artificial neural networks in applications for Building Information Modeling (BIM). The paper outlines the milestones reached in the last two decades. The article organizes the current state of knowledge and technology in terms of three aspects: (1) computer visualization coupled with BIM models (detection, segmentation, and quality verification in images, videos, and point clouds), (2) sequence and time series modeling (prediction of costs, energy, work progress, risk), and (3) integration of deep learning results with the semantics and topology of Industry Foundation Class (IFC) models. The paper identifies the most used architectures, typical data pipelines (synthetic data from BIM models, transfer learning, mapping results to IFC elements) and practical limitations: lack of standardized benchmarks, high annotation costs, a domain gap between synthetic and real data, and discontinuous interoperability. We indicate directions for development: combining CNN/RNN with graph models and transformers for wider use of synthetic data and semi-/supervised learning, as well as explainability methods that increase trust in AECOO (Architecture, Engineering, Construction, Owners & Operators) processes. A practical case study presents a new application, Bimetria, which uses a hybrid CNN/OCR (Optical Character Recognition) solution to generate 3D models with estimates based on two-dimensional drawings. A deep review shows that although the importance of attention-based and graph-based architectures is growing, CNNs and RNNs remain an important part of the BIM process, especially in engineering tasks, where, in our experience and in the Bimetria case study, mature convolutional architectures offer a good balance between accuracy, stability and low latency. The paper also raises some fundamental questions to which we are still seeking answers. Thus, the article not only presents the innovative new Bimetria tool but also aims to stimulate discussion about the dynamic development of AI (Artificial Intelligence) in BIM. Full article

(This article belongs to the Special Issue Modern Digital Technologies for the Built Environment of the Future)

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26 pages, 9440 KB

Open AccessArticle

Mitigating Urban Heat Island Effects Through Thermally Efficient Concrete Paver Blocks for Sustainable Infrastructure

by Tejas Joshi, Jeet Machchhoya, Urmil Dave, Plescan Costel and Vedanshi Shah

Infrastructures 2026, 11(1), 5; https://doi.org/10.3390/infrastructures11010005 - 21 Dec 2025

Abstract

Rapid urbanization and the widespread use of impervious materials have intensified the urban heat island (UHI) effect, raising surface temperatures and energy demands. Conventional concrete pavements contribute significantly due to their high thermal conductivity and low reflectivity. This study systematically investigates the development [...] Read more.

Rapid urbanization and the widespread use of impervious materials have intensified the urban heat island (UHI) effect, raising surface temperatures and energy demands. Conventional concrete pavements contribute significantly due to their high thermal conductivity and low reflectivity. This study systematically investigates the development of thermally efficient concrete paver blocks using sustainable alternative fine aggregates to mitigate heat accumulation while retaining a minimum compressive strength of 35–45 MPa (recommended for medium traffic). Unlike prior isolated studies, this research offers a comprehensive comparative analysis of three sand replacements—Vermiculite powder (12.5–50%), Perlite powder (20–80%), and Crushed Glass (7.5–30%)—in M30-grade concrete. Fresh and hardened properties were evaluated through slump, density, and compressive strength tests at 7, 14, and 28 days, while infrared thermography quantified surface temperature variations under controlled heat exposure. Results showed significant thermal improvements, with optimal mixes Vermiculite 25% (VC-25), Perlite 40% (PR-40), and Crushed Glass 15% (CG-15) reducing surface temperatures by 25.1 °C, 22.2 °C, and 18.2 °C, respectively, while maintaining compressive strengths of 47.8 MPa, 38.8 MPa, and ~58 MPa. VC-25 proved superior, achieving the lowest surface temperature (26.3 °C) and 48.8% lower heat absorption than conventional concrete. The study establishes optimal replacement thresholds balancing insulation and strength, supporting SDGs 11, 12, and 13 through climate-responsive, resource-efficient construction materials. Full article

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

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19 pages, 2740 KB

Open AccessArticle

Towards a Fair and Comprehensive Evaluation of Walkable Accessibility and Attractivity in the 15 Min City Scenario Based on Demographic Data

by Fabrizio Boninsegna, Alessandro Nalin, Andrea Simone, Bruno Zamengo, Denis Cappellari and Francesco Silvestri

Infrastructures 2026, 11(1), 4; https://doi.org/10.3390/infrastructures11010004 - 19 Dec 2025

Abstract

Accessibility and the so-called ‘15 min city’ paradigm are critical dimensions in agendas involving urban policies. However, when interested in accounting for accessibility from a formal perspective, researchers and practitioners should use pertinent indicators. Additionally, most of the indicators focus on the number [...] Read more.

Accessibility and the so-called ‘15 min city’ paradigm are critical dimensions in agendas involving urban policies. However, when interested in accounting for accessibility from a formal perspective, researchers and practitioners should use pertinent indicators. Additionally, most of the indicators focus on the number of facilities reachable within a given time window, while the counterpart of the latter, i.e., as a measure of attractiveness, such as the number of users that can reach that given area, is not evaluated explicitly. In this paper, a comprehensive method able to capture accessibility and attractivity simultaneously will be presented. The formulation is based on a refinement of the gravity model. As the main input, the actual number of residents was used and included in the computation. Therefore, the resulting values of accessibility and attractivity are intended to represent the real status of different degrees of walkable accessibility in urban areas. As a test field, three Italian cities were explored. The method proposed and discussed throughout the paper is aimed at providing an operative tool for planners, as well as for private stakeholders, when they are in charge of evaluating the degree of ‘walkable’ accessibility. Furthermore, the use of open and standardized data is intended to be a main strength of the proposed methodology, as it can be easily replicated in other contexts. Full article

(This article belongs to the Special Issue Sustainable Infrastructures for Urban Mobility, 2nd Edition)

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23 pages, 1232 KB

Open AccessArticle

Integrating System-Theoretic Process Analysis and System Dynamics for Systemic Risk Analysis in Safety-Critical Systems

by Ahmed Shaban, Ahmed Abdelwahed, Islam H. Afefy, Giulio Di Gravio and Riccardo Patriarca

Infrastructures 2026, 11(1), 3; https://doi.org/10.3390/infrastructures11010003 - 19 Dec 2025

Abstract

This paper presents a novel integration of System-Theoretic Process Analysis (STPA) and System Dynamics (SD) for hazard and resilience analysis in safety-critical infrastructure systems. The methodology is applied iteratively to assess the safety and continuity of a hospital’s oxygen supply system, a key [...] Read more.

This paper presents a novel integration of System-Theoretic Process Analysis (STPA) and System Dynamics (SD) for hazard and resilience analysis in safety-critical infrastructure systems. The methodology is applied iteratively to assess the safety and continuity of a hospital’s oxygen supply system, a key element of critical health infrastructure, addressing both technical and managerial factors. STPA identifies unsafe interactions between system components, which are systematically translated into a system dynamics simulation model. This dynamic perspective allows for the exploration of how hazards evolve over time and how control strategies influence overall system resilience. Unlike previous conceptual approaches, this study applies the integrated framework to a real-world incident of oxygen supply failure. The model structure is derived from STPA artifacts and validated using expert input and incident data. Simulation experiments uncovered emergent risk patterns, such as alarm delays, staff stress, and insufficient training, that are not evident through STPA alone. These insights support targeted interventions, including enhanced drill frequency and resource allocation, to strengthen infrastructure resilience. By embedding dynamic simulation within the STPA framework, this research moves beyond static hazard identification to enable scenario-based testing and conditional estimation of system response to support risk-informed decision-making. The resulting methodology is traceable, repeatable, and adaptable, offering a practical and generalizable tool for systemic risk analysis in critical infrastructures. Full article

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22 pages, 2385 KB

Open AccessArticle

Geotechnical Data Management for Infrastructure Resilience: A Relational Database Approach Based on the AGS Standard

by Gabriel de Oliveira Sant’Ana, Rejane Nascentes, Lucimara Bragagnolo, Eduardo Pavan Korf and Bruno Pereira de Queiroz

Infrastructures 2026, 11(1), 2; https://doi.org/10.3390/infrastructures11010002 - 19 Dec 2025

Abstract

Geotechnical information in digital formats remains difficult to access, as data are often dispersed across multiple sources, heterogeneous reports, and fragmented historical records. This fragmentation increases investigation costs, limits data reuse, and reduces the quality and reliability of engineering decisions. To address this [...] Read more.

Geotechnical information in digital formats remains difficult to access, as data are often dispersed across multiple sources, heterogeneous reports, and fragmented historical records. This fragmentation increases investigation costs, limits data reuse, and reduces the quality and reliability of engineering decisions. To address this challenge, this study aims to develop an open source digital system for storing and managing geotechnical information, structured around the international AGS standard to facilitate its use in both technical and academic applications. The methodology involved a review of existing geotechnical data management approaches followed by the construction of a relational model using an entity–relationship diagram, its transformation into a logical structure, and subsequent implementation in a database management system. After deployment, real geotechnical data were inserted to validate the system and generate simplified examples of practical use. The findings indicate that the AGS standard cannot be directly adopted as a database structure, requiring targeted adaptations to ensure efficient storage, validation, and querying. The developed system proved effective in standardizing, organizing, and retrieving geotechnical data, while also demonstrating versatility through seamless integration with Python-3.10 based analytical routines and GIS platforms. The main contribution of this research lies in presenting an open, modular, and AGS-aligned geotechnical data framework specifically adapted to the Brazilian context. While previous studies address important components of geotechnical data management, the approach presented here brings these elements together in a cohesive structure that supports reproducibility and future expansion. By detailing the rationale behind the digital adaptation of geotechnical information, the study provides a methodological path that supports replication and future system expansion. Full article

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23 pages, 2812 KB

Open AccessArticle

Laboratory Investigation of High-Temperature Rheological and Mechanical Properties of HDPE-Modified Bitumen

by Pooya Afkhamy Meybodi, Mohammad Mehdi Khabiri and Mehdi Entezam

Infrastructures 2026, 11(1), 1; https://doi.org/10.3390/infrastructures11010001 - 19 Dec 2025

Abstract

This study addresses the critical challenge of permanent deformation in asphalt pavements under high-temperature conditions by developing recycled high-density polyethylene (HDPE)-modified bitumen. Through systematic laboratory investigation, we quantified the dose-dependent effects of HDPE (2–9% wt.) on rheological and mechanical properties. Dynamic shear rheometry [...] Read more.

This study addresses the critical challenge of permanent deformation in asphalt pavements under high-temperature conditions by developing recycled high-density polyethylene (HDPE)-modified bitumen. Through systematic laboratory investigation, we quantified the dose-dependent effects of HDPE (2–9% wt.) on rheological and mechanical properties. Dynamic shear rheometry revealed a 472% increase in rutting resistance (G*/sinδ = 6.48 kPa) at 6% HDPE versus unmodified bitumen (1.13 kPa), alongside an 18–32% reduction in phase angle (58–88 °C). Rotational viscosity surged by 240% at 135 °C (1170 cP vs. 344 cP). Mechanically, Marshall Stability peaked at 19,000 N (46% enhancement) with 6% HDPE, while flow values minimized at 2.3 mm (15% reduction). Complementary tests confirmed superior temperature susceptibility control: penetration decreased by 50% and softening point increased by 43% (72.3 °C) at 9% HDPE, with Penetration Index shifting from −0.4 to +2.18. SEM microstructural analysis validated optimal polymer dispersion at 6%, forming a continuous reinforcing network, whereas agglomeration at higher doses induced defects. Statistical modeling identified a robust linear relationship for Marshall Quotient (Adjusted R² = 0.8383). The study establishes 6% HDPE as the optimal dosage, delivering synergistic high-temperature performance enhancement while utilizing recycled plastic. Future work should address long-term aging and field validation for sustainable pavement applications in tropical regions. Full article

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

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35 pages, 3452 KB

Open AccessArticle

Analyzing Natural Disaster Risk Factors in Engineering Projects: A Social Networks Analysis Approach

by Qiuyan Gu and Jun Wang

Infrastructures 2025, 10(12), 352; https://doi.org/10.3390/infrastructures10120352 - 18 Dec 2025

Abstract

Natural disasters pose significant risks to engineering projects, necessitating a systematic analysis of their risk factors. This study focuses on identifying and mapping these factors using a mixed-methods approach that integrates a qualitative literature review with scientometric analysis via Social Network Analysis (SNA). [...] Read more.

Natural disasters pose significant risks to engineering projects, necessitating a systematic analysis of their risk factors. This study focuses on identifying and mapping these factors using a mixed-methods approach that integrates a qualitative literature review with scientometric analysis via Social Network Analysis (SNA). Through a meta-analysis of 81 peer-reviewed articles from Web of Science, Scopus, and ScienceDirect, the qualitative review establishes a comprehensive list and classification of 48 natural disaster risk factors, categorized into geological, climatic, hydrological, topographic, and biological groups, while providing a theoretical foundation. SNA complements this by quantifying co-occurrence frequencies, centrality metrics (degree, betweenness, and eigenvector), and network structures, revealing dynamic interactions, key influential factors, and research gaps—particularly in under-explored areas like hydrological hazards, extreme temperatures, lightning storms, and temperature variations—that qualitative methods alone might miss. This multi-perspective integration highlights discrepancies between theoretical discussions and practical applications, underscoring overlooked cascading effects. Findings emphasize the absence of an integrated model for all 48 factors, urging the development of a holistic predictive framework to bolster disaster resilience. Theoretically, the study offers a novel SNA-based quantification of factor importance and interrelations, addressing literature fragmentation. Practically, it guides project managers in prioritizing risks for optimized design, resource allocation, and prevention strategies. Future research should incorporate real-time data sources to refine this framework for enhanced risk management in engineering projects. 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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19 pages, 7234 KB

Open AccessArticle

Temperature and Speed Corrections for TSD-Measured Deflection Slopes Using 3D Finite Element Simulations

by Nariman Kazemi, Mofreh Saleh and Chin-Long Lee

Infrastructures 2025, 10(12), 351; https://doi.org/10.3390/infrastructures10120351 - 16 Dec 2025

Abstract

Traffic Speed Deflectometer (TSD) measures deflection velocities, normalised by travel speed to obtain deflection slopes. Pavement temperature and travel speed can significantly affect deflection slopes. Therefore, correcting deflection slopes for temperature and speed effects is essential. This study employs three-dimensional (3D) finite element [...] Read more.

Traffic Speed Deflectometer (TSD) measures deflection velocities, normalised by travel speed to obtain deflection slopes. Pavement temperature and travel speed can significantly affect deflection slopes. Therefore, correcting deflection slopes for temperature and speed effects is essential. This study employs three-dimensional (3D) finite element simulations of a three-layer flexible pavement system subjected to moving load at travel speeds from 40 km/h to 80 km/h, while varying the Asphalt Concrete (AC) layers’ thickness from 100 mm to 300 mm and the temperature from 5 °C to 45 °C. The results showed that deflection slopes at 100 mm offset distance could be corrected for the effects of temperature and speed using a correction factor comprising the sum of a parabolic function of temperature and a linear function of speed. At 600 mm and 1500 mm offset distances, simpler correction factors could be established using the sum of linear functions of temperature and speed. The Mean Absolute Percentage Error (MAPE) for all predictions was below 3%, indicating high accuracy. Accurate regression-based equations were also proposed to incorporate AC thickness in predicting the correction factors. The results highlight the potential to correct deflection slopes to a reference temperature and speed by evaluating a range of pavement systems. Full article

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23 pages, 2895 KB

Open AccessArticle

Impact of Pavement Surface Roughness on TSD Backcalculation Outputs and Potential Mitigation Strategies

by Nariman Kazemi, Mofreh Saleh and Chin-Long Lee

Infrastructures 2025, 10(12), 350; https://doi.org/10.3390/infrastructures10120350 - 16 Dec 2025

Abstract

Deflection slopes measured by the traffic speed deflectometer (TSD) are being used to backcalculate the moduli of pavement layers. Pavement surface roughness causes variations in tyre load magnitude due to excitation, which affects TSD measurements. In this study, three rough pavement surface profiles [...] Read more.

Deflection slopes measured by the traffic speed deflectometer (TSD) are being used to backcalculate the moduli of pavement layers. Pavement surface roughness causes variations in tyre load magnitude due to excitation, which affects TSD measurements. In this study, three rough pavement surface profiles over 150 m longitudinal distances were extracted from the Long-Term Pavement Performance (LTPP) programme database. Utilising finite element method (FEM) simulation of the TSD pass at a travel speed of 80 km/h over a three-layer flexible pavement system containing the rough surface profiles and employing the Greenwood Engineering TSD backcalculation tool, it was found that tyre load excitation can lead to backcalculation errors of up to 48%. By obtaining deflection slopes at equal distance intervals along the 150 m pavement profiles, it was found that averaging the deflection slopes across 9 measurement points reduced backcalculation errors to 10%, while increasing the number of measurement points to 28 further lowered the backcalculation errors to 5%. These findings highlight the potential to mitigate the effects of tyre load excitation on TSD backcalculation outputs without relying on strain gauges, which are mounted on modern TSDs to measure instantaneous tyre load magnitudes but are sensitive to environmental conditions and require calibration. Full article

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

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25 pages, 5082 KB

Open AccessArticle

Performance Evaluation of Fixed-Point DFOS Cables for Structural Monitoring of Reinforced Concrete Elements

by Aigerim Buranbayeva, Assel Sarsembayeva, Bun Pin Tee, Iliyas Zhumadilov and Gulizat Orazbekova

Infrastructures 2025, 10(12), 349; https://doi.org/10.3390/infrastructures10120349 - 15 Dec 2025

Abstract

Distributed fiber-optic sensing (DFOS) with intentionally spaced mechanical fixity points was experimentally evaluated for the structural health monitoring (SHM) of reinforced concrete (RC) members. A full-scale four-point bending test was conducted on a 12 m RC beam (400 × 400 mm) instrumented with [...] Read more.

Distributed fiber-optic sensing (DFOS) with intentionally spaced mechanical fixity points was experimentally evaluated for the structural health monitoring (SHM) of reinforced concrete (RC) members. A full-scale four-point bending test was conducted on a 12 m RC beam (400 × 400 mm) instrumented with a single-mode DFOS cable incorporating internal anchors at 2 m intervals and bonded externally with structural epoxy. Brillouin time-domain analysis (BOTDA) provided distributed strain measurements at approximately 0.5 m spatial resolution, with all cables calibrated to ±15,000 µε. Under stepwise monotonic loading, the system captured smooth, repeatable strain baselines and clearly resolved localized tensile peaks associated with crack initiation and propagation. Long-gauge averages exhibited a near-linear load–strain response (R² ≈ 0.99) consistent with discrete foil and vibrating-wire strain gauges. Even after cracking, the DFOS signal remained continuous, while some discrete sensors showed saturation or scatter. Temperature compensation via a parallel fiber ensured thermally stable interpretation during load holds. The fixed-point configuration mitigated local debonding effects and yielded unbiased long-gauge strain data suitable for assessing serviceability and differential settlement. Overall, the results confirm the suitability of fixed-point DFOS as a durable, SHM-ready sensing approach for RC foundation elements and as a dense data source for emerging digital-twin frameworks. Full article

(This article belongs to the Special Issue Advanced Sensing Technologies and Smart Construction Materials for Structural Health Monitoring)

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20 pages, 14411 KB

Open AccessArticle

An Integrated Framework with SAM and OCR for Pavement Crack Quantification and Geospatial Mapping

by Nut Sovanneth, Asnake Adraro Angelo, Felix Obonguta and Kiyoyuki Kaito

Infrastructures 2025, 10(12), 348; https://doi.org/10.3390/infrastructures10120348 - 15 Dec 2025

Abstract

Pavement condition assessment using computer vision has emerged as an efficient alternative to traditional manual surveys, which are often labor-intensive and time-consuming. Leveraging deep learning, pavement distress such as cracks can be automatically detected, segmented, and quantified from high-resolution images captured by survey [...] Read more.

Pavement condition assessment using computer vision has emerged as an efficient alternative to traditional manual surveys, which are often labor-intensive and time-consuming. Leveraging deep learning, pavement distress such as cracks can be automatically detected, segmented, and quantified from high-resolution images captured by survey vehicles. Although numerous segmentation models have been proposed to generate crack masks, they typically require extensive pixel-level annotations, leading to high labeling costs. To overcome this limitation, this study integrates the Segmentation Anything Model (SAM), which produces accurate segmentation masks from simple bounding box prompts while leveraging its zero-shot capability to generalize to unseen images with minimal retraining. However, since SAM alone is not an end-to-end solution, we incorporate YOLOv8 for automated crack detection, eliminating the need for manual box annotation. Furthermore, the framework applies local refinement techniques to enhance mask precision and employs Optical Character Recognition (OCR) to automatically extract embedded GPS coordinates for geospatial mapping. The proposed framework is empirically validated using open-source pavement images from Yamanashi, demonstrating effective automated detection, classification, quantification, and geospatial mapping of pavement cracks. The results support automated pavement distress mapping onto real-world road networks, facilitating efficient maintenance planning for road agencies. Full article

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22 pages, 2039 KB

Open AccessReview

The Impact of Autonomous Vehicles on the Transportation Network with a Focus on the Physical Road Infrastructure

by Ana Čudina Ivančev, Tamara Džambas and Vesna Dragčević

Infrastructures 2025, 10(12), 347; https://doi.org/10.3390/infrastructures10120347 - 14 Dec 2025

Abstract

Significant progress in autonomous vehicle (AV) development has been made over the years through advancements in artificial intelligence, sensor technology, and data processing; however, many challenges remain, particularly regarding road safety and the complexity of adapting these vehicles to certain traffic situations. As [...] Read more.

Significant progress in autonomous vehicle (AV) development has been made over the years through advancements in artificial intelligence, sensor technology, and data processing; however, many challenges remain, particularly regarding road safety and the complexity of adapting these vehicles to certain traffic situations. As a result, many European countries are funding research projects and setting targets and strategic plans for autonomous mobility, while scientific research proposes establishing standards and design guidelines for adapting road infrastructure to new transportation trends. This review paper examines physical road infrastructure in the era of AVs and identifies potential modifications, considering the development of AVs during both the early and later stages of their introduction into mixed traffic flow. Accordingly, necessary road infrastructure adaptations and the main design parameters affecting road geometric design for AV operation are presented. The design parameters considered include stopping sight distance, vertical curve radii, straight sections, lanes, and others. Furthermore, potential changes in existing physical infrastructure are illustrated using the example of a deceleration lane. Whether it is new infrastructure or modifications to existing infrastructure, both are analyzed in terms of the proportion of AVs in the traffic flow. Full article

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

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23 pages, 2331 KB

Open AccessArticle

Life Cycle Impacts of Timber and Reinforced Concrete Floor Slabs: A Comparative Assessment

by Honghao Ren, Marita Wallhagen, Alireza Bahrami and Mathias Cehlin

Infrastructures 2025, 10(12), 346; https://doi.org/10.3390/infrastructures10120346 - 13 Dec 2025

Abstract

Due to their sustainability, lightweight qualities, and simplicity of installation, wood slab systems have gained increasing attention in the building industry. Cross-laminated timber (CLT), an engineered wood product (EWP), improves structural strength and stability, offering a good alternative to conventional reinforced concrete (RC) [...] Read more.

Due to their sustainability, lightweight qualities, and simplicity of installation, wood slab systems have gained increasing attention in the building industry. Cross-laminated timber (CLT), an engineered wood product (EWP), improves structural strength and stability, offering a good alternative to conventional reinforced concrete (RC) slab systems. Conventional CLT, however, contains adhesives that pose environmental and end-of-life (EOL) disposal challenges. Adhesive-free CLT (AFCLT) panels have recently been introduced as a sustainable option, but their environmental performance has not yet been thoroughly investigated. In this study, the environmental impacts of five slab systems are evaluated and compared using the life cycle assessment (LCA) methodology. The investigated slab systems include a standard CLT slab (SCLT), three different AFCLT slabs (AFCLT1, AFCLT2, and AFCLT3), and an RC slab. The assessment considered abiotic depletion potential (ADP), global warming potential (GWP), ozone layer depletion potential (ODP), human toxicity potential (HTP), freshwater aquatic ecotoxicity potential (FAETP), marine aquatic ecotoxicity potential (MAETP), terrestrial ecotoxicity potential (TETP), photochemical oxidation potential (POCP), acidification potential (AP), and eutrophication potential (EP), covering the entire life cycle from production to disposal, excluding part of the use stage (B2-B7). The results highlight the advantages and drawbacks of each slab system, providing insights into selecting sustainable slab solutions. AFCLT2 exhibited the lowest environmental impacts across the assessed categories. On the contrary, the RC slab showed the highest environmental impact among the studied products. For example, the RC slab had the highest GWP of 67.422 kg CO₂ eq, which was 1784.3% higher than that of AFCLT2 (3.779 kg CO₂ eq). Additionally, the simulation displayed that the analysis results vary depending on the electricity source, which is influenced by geographical location. Using the Norwegian electricity mix resulted in the most sustainable outcomes compared with Sweden, Finland, and Saudi Arabia. This study contributes to the advancement of low-carbon construction techniques and the development of building materials with reduced environmental impacts in the construction sector. Full article

(This article belongs to the Section Sustainable Infrastructures)

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38 pages, 967 KB

Open AccessReview

Environmentally Sustainable and Climate-Adapted Bitumen–Composite Materials for Road Construction in Central Asia

by Gulbarshin K. Shambilova, Rinat M. Iskakov, Nurgul K. Shazhdekeyeva, Bayan U. Kuanbayeva, Mikhail S. Kuzin, Ivan Yu. Skvortsov and Igor S. Makarov

Infrastructures 2025, 10(12), 345; https://doi.org/10.3390/infrastructures10120345 - 12 Dec 2025

Abstract

This review examines scientific and engineering strategies for adapting bituminous and asphalt concrete materials to the highly diverse climates of Central Asia. The region’s sharp gradients—from arid lowlands to cold mountainous zones—expose pavements to thermal fatigue, photo-oxidative aging, freeze–thaw cycles, and wind abrasion. [...] Read more.

This review examines scientific and engineering strategies for adapting bituminous and asphalt concrete materials to the highly diverse climates of Central Asia. The region’s sharp gradients—from arid lowlands to cold mountainous zones—expose pavements to thermal fatigue, photo-oxidative aging, freeze–thaw cycles, and wind abrasion. Existing climatic classifications and principles for designing thermally and radiatively resilient pavements are summarized. Special emphasis is placed on linking binder morphology, rheology, and climate-induced transformations in composite bituminous systems. Advanced characterization methods—including dynamic shear rheometry (DSR), multiple stress creep recovery (MSCR), bending beam rheometry (BBR), and linear amplitude sweep (LAS), supported by FTIR, SEM, and AFM—enable quantitative correlations between phase composition, oxidative chemistry, and mechanical performance. The influence of polymeric, nanostructured, and biopolymeric modifiers on stability and durability is critically assessed. The review promotes region-specific material design and the use of integrated accelerated aging protocols (RTFOT, PAV, UV, freeze–thaw) that replicate local climatic stresses. A climatic rheological profile is proposed as a unified framework combining climate mapping with microstructural and rheological data to guide the development of sustainable and durable pavements for Central Asia. Key rheological indicators—complex modulus (G*), non-recoverable creep compliance (Jnr), and the BBR m-value—are incorporated into this profile. Full article

(This article belongs to the Special Issue Safer Roads Ahead: Exploring the Latest Innovations and Advancements in Road Design and Safety Technology)

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