Infrastructures

24 pages, 13834 KiB

Open AccessArticle

Development of a Stress Block Model to Predict the Ultimate Bending Capacity of Rectangular Concrete-Filled Steel Tube Beams Strengthened with U-Shaped CFRP Sheets

by Mohammad Ansari, Ahmed W. Al Zand, Emad Hosseinpour, Ali Joharchi and Masoud Abedini

Infrastructures 2025, 10(4), 73; https://doi.org/10.3390/infrastructures10040073 (registering DOI) - 24 Mar 2025

Abstract

The prediction of the ultimate bending capacity of the rectangular concrete-filled steel tube (RCFST) beams strengthened with U-shaped carbon fiber reinforced polymer (CFRP) sheets is limited to using the existing empirical models. Thus, this study aims to develop a new theoretical model based [...] Read more.

The prediction of the ultimate bending capacity of the rectangular concrete-filled steel tube (RCFST) beams strengthened with U-shaped carbon fiber reinforced polymer (CFRP) sheets is limited to using the existing empirical models. Thus, this study aims to develop a new theoretical model based on a stress block model to predict the ultimate bending capacity (M_u) of the RCFST beams strengthened with a U-shaped CFRP-wrapping scheme. For this purpose, 28 finite element (FE) models of CFRP-strengthened RCFST beams had been analyzed for further investigation of the flexural behavior and longitudinal stresses distributed along with the beam’s components (steel tube, concrete core, and CFRP layers). The main parameters investigated are concrete compressive strength, steel yield strength, number of CFRP layers, and CFRP-wrapping-depth ratio. In addition, the M_u values obtained from the FE models of the current study and those from the existing experimental tests performed by others are used to verify the corresponding values that are theoretically predicted by the new model. The comparison showed that the proposed model is moderately conservative, as the predicted values of M_u are, on average, up to 10% lower than those obtained from experimental tests and FE analysis. Full article

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

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18 pages, 10235 KiB

Open AccessArticle

Research on Asphalt Pavement Surface Distress Detection Technology Coupling Deep Learning and Object Detection Algorithms

by Hong Zhang, Yuanshuai Dong, Yun Hou, Xiangjun Cheng, Peiwen Xie and Keming Di

Infrastructures 2025, 10(4), 72; https://doi.org/10.3390/infrastructures10040072 (registering DOI) - 24 Mar 2025

Abstract

To address the challenges posed by the vast scale of highway maintenance in China and the high costs associated with traditional inspection vehicles. This study focuses on a routine maintenance project for national and provincial roads in Shanxi Province, with an emphasis on [...] Read more.

To address the challenges posed by the vast scale of highway maintenance in China and the high costs associated with traditional inspection vehicles. This study focuses on a routine maintenance project for national and provincial roads in Shanxi Province, with an emphasis on the selection and design of hardware for lightweight, portable pavement inspection devices. A monocular camera was used to capture pavement surface images, resulting in a dataset of 85,511 training samples. Additionally, the YOLOv5 object detection algorithm, combined with convolutional deep learning techniques, was employed to classify and identify pavement surface distresses in the collected images. Through multiple iterations of model tuning and validation, the proposed detection system achieved a false negative rate of 1.13%, a recall rate of 97.35%, and a precision rate of 98.30%. Its high accuracy provides a technical reference for the development and design of portable pavement distress detection devices. Full article

(This article belongs to the Special Issue Sustainable and Digital Transformation of Road Infrastructures)

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18 pages, 9282 KiB

Open AccessArticle

Parametric Analysis as a Tool for Hypothesis Generation: A Case Study of the Federal Archive Building in New York City

by Mike Christenson

Infrastructures 2025, 10(4), 71; https://doi.org/10.3390/infrastructures10040071 (registering DOI) - 24 Mar 2025

Abstract

This study investigates the epistemological potentials of parametric analysis for digitally modeling ordinary, existing buildings, addressing a gap in architectural research. While traditional digital modeling prioritizes geometric accuracy, it often limits the ability to generate new architectural insights, treating models as static representations [...] Read more.

This study investigates the epistemological potentials of parametric analysis for digitally modeling ordinary, existing buildings, addressing a gap in architectural research. While traditional digital modeling prioritizes geometric accuracy, it often limits the ability to generate new architectural insights, treating models as static representations rather than as tools for knowledge production. This research challenges the assumption that geometric accuracy is necessary for epistemological validity, proposing parametric analysis as a hypothesis-generating tool capable of uncovering latent spatial and morphological properties that conventional methods overlook. Using Suárez’s inferential conception of scientific representation as a theoretical framework, this research employs a comparative case study methodology, contrasting direct and parametric digital models of the Federal Archive Building in New York City, analyzing their respective contributions to architectural knowledge. Existing documentation of the Federal Archive Building provides the primary data. The findings reveal that parametric modeling can enable the discovery of latent design properties by facilitating the systematic exploration of geometric variations while maintaining other logics, specifically by demonstrating how certain architectural features accommodate site irregularities while preserving visual coherence. This research advances theoretical discourse by repositioning parametric models from descriptive artifacts to instruments of architectural reasoning, challenging conventional associations between representational accuracy and epistemological validity. Practical applications are suggested in heritage documentation, comparative architectural analysis, and educational contexts where the interpretive exploration of buildings can generate new insights beyond what geometrically accurate models alone can provide. Full article

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

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17 pages, 6954 KiB

Open AccessArticle

Point Transformer Network-Based Surrogate Model for Spatial Prediction in Bridges

by Javier Grandío, Brais Barros, Manuel Cabaleiro and Belén Riveiro

Infrastructures 2025, 10(4), 70; https://doi.org/10.3390/infrastructures10040070 (registering DOI) - 22 Mar 2025

Abstract

Bridges are essential assets of inland transportation infrastructure; however, they are among the most vulnerable elements of these networks due to deterioration caused by aging and the increasing loads to which they are subjected over time. Consequently, maintenance becomes critical to ensure acceptable [...] Read more.

Bridges are essential assets of inland transportation infrastructure; however, they are among the most vulnerable elements of these networks due to deterioration caused by aging and the increasing loads to which they are subjected over time. Consequently, maintenance becomes critical to ensure acceptable levels of safety and service. Finite element (FE) models are traditionally used to reliably assess structural health, but their computational expense often prevents their extensive use in routine bridge assessments. To overcome this computational limitation, this paper presents an innovative deep learning-based surrogate model for predicting local displacements in bridge structures. By utilizing point cloud data and transformer neural networks, the model provides fast and accurate predictions of displacements, addressing the limitations of traditional methods. A case study of a historical bridge demonstrates the model’s efficiency. The proposed approach integrates spatial data processing techniques, offering a computationally efficient alternative for bridge health monitoring. Our results show that the model achieves mean absolute errors below 0.0213 mm, drastically reducing the time required for structural analysis. Full article

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

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16 pages, 3637 KiB

Open AccessArticle

Development of a Large Database of Italian Bridge Bearings: Preliminary Analysis of Collected Data and Typical Defects

by Angelo Masi, Giuseppe Santarsiero, Marco Savoia, Enrico Cardillo, Beatrice Belletti, Ruggero Macaluso, Maurizio Orlando, Giovanni Menichini, Giacomo Morano, Giuseppe Carlo Marano, Fabrizio Palmisano, Anna Saetta, Luisa Berto, Maria Rosaria Pecce, Antonio Bilotta, Pier Paolo Rossi, Andrea Floridia, Mauro Sassu, Marco Zucca, Eugenio Chioccarelli, Alberto Meda, Daniele Losanno, Marco Di Prisco, Giorgio Serino, Paolo Riva, Nicola Nisticò, Sergio Lagomarsino, Stefania Degli Abbati, Giuseppe Maddaloni, Gennaro Magliulo, Mattia Calò, Fabio Biondini, Francesca da Porto, Daniele Zonta and Maria Pina Limongelli Show full author list Hide full author list

Infrastructures 2025, 10(3), 69; https://doi.org/10.3390/infrastructures10030069 - 20 Mar 2025

Abstract

This paper presents the development and analysis of a bridge bearing database consistent with the 2020 Italian Guidelines (LG2020), currently enforced by the Italian law for risk classification and management of existing bridges. The database was developed by putting together the contribution of [...] Read more.

This paper presents the development and analysis of a bridge bearing database consistent with the 2020 Italian Guidelines (LG2020), currently enforced by the Italian law for risk classification and management of existing bridges. The database was developed by putting together the contribution of 24 research teams from 18 Italian universities in the framework of a research project foreseen by the agreement between the High Council of Public Works (CSLP, part of the Italian Ministry of Transportation) and the research consortium ReLUIS (Network of Italian Earthquake and Structural Engineering University Laboratories). This research project aimed to apply LG2020 to a set of about 600 bridges distributed across the Italian country, in order to find possible issues and propose modifications and integrations. The database includes almost 12,000 bearing defect forms related to a portfolio of 255 existing bridges located across the entire country. This paper reports a preliminary analysis of the dataset to provide an overview of the bearings installed in a significant bridge portfolio, referring to major highways and state roads. After a brief state of the art about the main bearing types installed on the bridges, along with inspection procedures, the paper describes the database structure, showing preliminary analyses related to bearing types and defects. The results show the prevalence of elastomeric pads, representing more than 55% of the inspected bearings. The remaining bearings are pot, low-friction with steel–Teflon surfaces and older-type steel devices. Lastly, the study provides information about typical defects for each type of bearing, while also underscoring some issues related to the current version of the LG2020 bearing inspection form. Full article

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17 pages, 4387 KiB

Open AccessArticle

Failure Mode- and Time-Dependent Reliability Model of Tunnel Lining Structure Under Extremely High Ground Stress

by Tao Peng, Dongxing Ren, Fanmin He, Binjia Li, Fei Wu and Shijie Xu

Infrastructures 2025, 10(3), 68; https://doi.org/10.3390/infrastructures10030068 - 20 Mar 2025

Abstract

Damage to tunnel lining significantly influences the stability of tunnels during operation, particularly under conditions of extra-high ground stress. This article investigates the stability of tunnel linings subjected to extra-high ground stress, providing an in-depth analysis of crack damage modes. A time-varying reliability [...] Read more.

Damage to tunnel lining significantly influences the stability of tunnels during operation, particularly under conditions of extra-high ground stress. This article investigates the stability of tunnel linings subjected to extra-high ground stress, providing an in-depth analysis of crack damage modes. A time-varying reliability model based on the structural performance function is proposed, which incorporates the effects of the plastic zone and the identified crack damage modes. The plastic zone and the distribution of the surrounding rock stress field throughout the excavation process were simulated, elucidating the relationship between vault displacement and stress release rate. The time-varying reliability model is employed to assess lining behavior under extremely high ground stress and to establish the patterns governing its service life. The findings of this study offer a crucial reference for further investigations into the time-varying reliability of tunnel linings in the context of extreme ground stress. Full article

(This article belongs to the Topic Development of Underground Space for Engineering Application)

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26 pages, 3014 KiB

Open AccessReview

Shear Behavior of Ultra-High-Performance Concrete Deep Beams Reinforced with Fibers: A State-of-the-Art Review

by Hossein Mirzaaghabeik, Nuha S. Mashaan and Sanjay Kumar Shukla

Infrastructures 2025, 10(3), 67; https://doi.org/10.3390/infrastructures10030067 - 20 Mar 2025

Abstract

Ultra-high-performance concrete (UHPC) is considered a highly applicable composite material due to its exceptional mechanical properties, such as high compressive strength and ductility. UHPC deep beams are structural elements suitable for short spans, transfer girders, pile caps, offshore platforms, and bridge applications where [...] Read more.

Ultra-high-performance concrete (UHPC) is considered a highly applicable composite material due to its exceptional mechanical properties, such as high compressive strength and ductility. UHPC deep beams are structural elements suitable for short spans, transfer girders, pile caps, offshore platforms, and bridge applications where they are designed to carry heavy loads. Several key factors significantly influence the shear behavior of UHPC deep beams, including the compressive strength of UHPC, the vertical web reinforcement (ρ_sv), horizontal web reinforcement (ρ_sh), and longitudinal reinforcement (ρ_s), as well as the shear span-to-depth ratio (λ), fiber type, fiber content (FC), and geometrical dimensions. In this paper, a comprehensive literature review was conducted to evaluate factors influencing the shear behavior of UHPC deep beams, with the aim of identifying research gaps and enhancing understanding of these influences. The findings from the literature were systematically classified and analyzed to clarify the impact and trends associated with each factor. The analyzed data highlight the effect of each factor on the shear behavior of UHPC deep beams, along with the overall trends. The findings indicate that an increase in compressive strength, FC, ρ_sv, ρ_s, and ρ_sh can enhance the shear capacity of UHPC-DBs by up to 63.36%, 63.24%, 38.14%, 19.02%, and 38.14%, respectively. Additionally, a reduction of 61.29% in λ resulted in a maximum increase of 49.29% in the shear capacity of UHPC-DBs. Full article

(This article belongs to the Topic Advances on Structural Engineering, 3rd Edition)

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27 pages, 5217 KiB

Open AccessReview

The Use of Earth Observation Data for Railway Infrastructure Monitoring—A Review

by Milan Banic, Danijela Ristic-Durrant, Milos Madic, Alina Klapper, Milan Trifunovic, Milos Simonovic and Szabolcs Fischer

Infrastructures 2025, 10(3), 66; https://doi.org/10.3390/infrastructures10030066 - 19 Mar 2025

Abstract

Satellite data have the potential to significantly enhance railway operations and drive the digitization of the rail sector. In the context of railways, satellite data primarily refers to the use of Global Navigation Satellite System (GNSS) data for applications such as navigation, positioning, [...] Read more.

Satellite data have the potential to significantly enhance railway operations and drive the digitization of the rail sector. In the context of railways, satellite data primarily refers to the use of Global Navigation Satellite System (GNSS) data for applications such as navigation, positioning, and signalling. However, remote sensing data from Earth Observation (EO) satellites remain comparatively underutilized in railway applications. While the use of GNSS data in railways is well documented in the literature, research on EO-based remote sensing methods remains relatively limited. This paper aims to bridge this gap as it presents a comprehensive review of the use of satellite data in railway applications, with a particular focus on the underexplored potential of EO data. It provides the first in-depth analysis of EO techniques, primarily examining the use of synthetic aperture radar (SAR) and optical satellite data for key applications for infrastructure managers and railway operators, such as assessing track stability, detecting deformations, and monitoring surrounding environmental conditions. The goal of this review is to explore the diverse range of EO-based applications in railways and to identify emerging trends, including the integration of thermal EO data and the novel use of SAR for dynamic and predictive analyses. By synthesizing existing research and addressing knowledge gaps, the presented review underscores the potential of EO data to transform railway infrastructure management. Enhanced spatial resolution, frequent revisit cycles, and advanced AI-driven analytics are highlighted as key enablers for safer, more reliable, and cost-effective solutions. This review provides a framework for leveraging EO data to drive innovation and improve railway monitoring practices. Full article

(This article belongs to the Special Issue Emerging Technologies for Effective and Intelligent Transport Infrastructure Monitoring)

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16 pages, 1863 KiB

Open AccessArticle

Determining Passing Sight Distance on Upgraded Road Sections over Single and Platooned Heavy Military Vehicles

by Stergios Mavromatis, Vassilios Matragos, Antonis Kontizas and Kiriakos Amiridis

Infrastructures 2025, 10(3), 65; https://doi.org/10.3390/infrastructures10030065 - 19 Mar 2025

Abstract

Although truck platooning enhances transportation efficiency, reduces fuel consumption, and lowers freight transport costs, it can also create limited overtaking opportunities, potentially leading to risky overtaking maneuvers. The present study examines the impact of platooned heavy military vehicles on the quantification of Passing [...] Read more.

Although truck platooning enhances transportation efficiency, reduces fuel consumption, and lowers freight transport costs, it can also create limited overtaking opportunities, potentially leading to risky overtaking maneuvers. The present study examines the impact of platooned heavy military vehicles on the quantification of Passing Sight Distance (PSD). Two distinct cases are examined: single and platooned military vehicles passing, the latter formed by five trucks. The authors, by realistically modeling the passing task, examined the interaction between vehicle dynamic parameters and roadway grade utilizing an existing vehicle dynamics model. The analysis of various speed values revealed significant PSD variations depending on the examined impeding (overtaken) vehicle’s platooning configuration and utilized grade. The present assessment accurately quantifies the grade impact on the required PSDs for such special vehicle arrangements and can be applied to any vehicle platooning configuration. Moreover, a preliminary tool is introduced to assist road designers in accurately assessing the impact of roadway grade on the passing process. This tool, when combined with a more in-depth analysis of additional factors, can help justify the need for an extra lane in road sections where platooning regularly occurs. Full article

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

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36 pages, 11633 KiB

Open AccessReview

Review and Insights Toward Cognitive Digital Twins in Pavement Assets for Construction 5.0

by Mohammad Oditallah, Morshed Alam, Palaneeswaran Ekambaram and Sagheer Ranjha

Infrastructures 2025, 10(3), 64; https://doi.org/10.3390/infrastructures10030064 - 15 Mar 2025

Abstract

With the movement of the construction industry towards Construction 5.0, Digital Twin (DT) has emerged in recent years as a pivotal and comprehensive management tool for predictive strategies for infrastructure assets. However, its effective adoption and conceptual implementation remain limited in this domain. [...] Read more.

With the movement of the construction industry towards Construction 5.0, Digital Twin (DT) has emerged in recent years as a pivotal and comprehensive management tool for predictive strategies for infrastructure assets. However, its effective adoption and conceptual implementation remain limited in this domain. Current review works focused on applications and potentials of DT in general infrastructures. This review focuses on interpreting DT’s conceptual foundation in the flexible pavement asset context, including core components, considerations, and methodologies. Existing pavement DT implementations are evaluated to uncover their strengths, limitations, and potential for improvement. Based on a systematic review, this study proposes a comprehensive cognitive DT framework for pavement management. It explores the extent of enhanced decision-making and a large-scale collaborative DT environment. This study also identifies current and emerging challenges and enablers, as well as highlights future research directions to advance DT implementation and support its alignment with the transformative goals of Construction 5.0. Full article

(This article belongs to the Special Issue Sustainable and Digital Transformation of Road Infrastructures)

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25 pages, 3743 KiB

Open AccessArticle

Cost Efficiency and Effectiveness of Drone Applications in Bridge Condition Monitoring

by Taraneh Askarzadeh and Raj Bridgelall

Infrastructures 2025, 10(3), 63; https://doi.org/10.3390/infrastructures10030063 - 13 Mar 2025

Abstract

Bridges are an integral and important part of road networks, but monitoring their condition using traditional methods is expensive, dangerous, and laborious. This study examines the rapidly emerging field of drone-based transportation asset monitoring, focusing on analyzing the cost efficiency and effectiveness of [...] Read more.

Bridges are an integral and important part of road networks, but monitoring their condition using traditional methods is expensive, dangerous, and laborious. This study examines the rapidly emerging field of drone-based transportation asset monitoring, focusing on analyzing the cost efficiency and effectiveness of drone applications in bridge condition monitoring. This research innovated a multi-dimensional framework that highlights the transformative role of drone technology in enhancing inspection accuracy, safety, and cost savings. Using statistical models and Monte Carlo simulations, the framework provides an extensive cost–benefit analysis to inform drone investment decisions. A case study demonstrates the utility of the framework in quantifying costs and benefits. Furthermore, a sensitivity analysis evaluates how variations in drone costs, driven by technological progress, can potentially influence adoption of the technology. Full article

(This article belongs to the Special Issue Sustainable and Digital Transformation of Road Infrastructures)

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19 pages, 5841 KiB

Open AccessArticle

Comparative Analysis of Soft Clay Improvement Using Ordinary and Grouted Sand Columns with Geosynthetic Reinforcement

by Mohammed Y. Fattah, Muthanna A. Al-Khafaji, Makki K. Mohsen and Mohamed Hafez

Infrastructures 2025, 10(3), 62; https://doi.org/10.3390/infrastructures10030062 - 13 Mar 2025

Abstract

Soft clay soil is known for its high compressibility and low bearing capacity, making it one of the most challenging soil types. Sand columns and sand layers reinforced with geosynthetics are effective techniques to enhance the performance of foundations built on soft clay. [...] Read more.

Soft clay soil is known for its high compressibility and low bearing capacity, making it one of the most challenging soil types. Sand columns and sand layers reinforced with geosynthetics are effective techniques to enhance the performance of foundations built on soft clay. Stone or sand columns improve load-bearing capacity by utilizing the natural lateral confinement of the soil. However, in very soft soil, a significant design challenge arises due to bulging in the stone columns, as the surrounding soil may not provide adequate confinement to support the required load capacity. This issue has been addressed by grouting the columns, resulting in highly stable and solid structures. Additionally, the grouting pressure enhances frictional resistance and fills any voids within the soil, contributing to increased overall stability. In the current study, soil improvement methods using ordinary sand columns and grouted sand columns were investigated and then compared with adding sand layers with geogrid reinforcement. The study demonstrated that grouted sand columns improved the bearing capacity by 90% over untreated clay. With geogrid reinforcement, sand columns achieved a 180% increase, while grouted columns with geogrid reinforcement reached a 260% improvement. Increasing the thickness of reinforced sand (H/B = 1.5) further raised capacity improvements to 300% for ungrouted and 420% for grouted columns. Full article

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13 pages, 1998 KiB

Open AccessArticle

Optimizing Equivalent Property-Damage-Only (EPDO) Prediction Models with Genetic Algorithms: A Case Study on Roundabout Geometric Characteristics

by Hossein Samadi, Omid Rahmani, Khaled Shaaban, Amir Saman Abdollahzadeh Nasiri and Mehrzad Hasanvand

Infrastructures 2025, 10(3), 61; https://doi.org/10.3390/infrastructures10030061 - 10 Mar 2025

Abstract

Roundabouts generally offer better traffic safety than other intersections, yet severe crashes still occur. They serve as a viable option to enhance intersection safety and reduce crash severity. Improving crash prediction models enhances the precision of prioritization and safety evaluation, ultimately lowering crash-related [...] Read more.

Roundabouts generally offer better traffic safety than other intersections, yet severe crashes still occur. They serve as a viable option to enhance intersection safety and reduce crash severity. Improving crash prediction models enhances the precision of prioritization and safety evaluation, ultimately lowering crash-related costs. This study examines the impact of geometric factors on crash frequency and severity in roundabouts. The equivalent property-damage-only (EPDO) index, which considers both severity and frequency, was included as an independent parameter. Increasing traffic volume significantly affects crash numbers, often overshadowing other contributing factors. This study investigates the effects of central island radius (R), average weaving section width (AWWS), and average entry width (AEW) on crashes. To achieve this, data from four roundabouts were analyzed using Gene Expression Programming (GEP) to develop a predictive model. The model achieved a 99% correlation coefficient, effectively capturing data dispersion. The results showed that R accounted for over 75% of the variance, making it the most influential geometric parameter. The proposed procedure can significantly assist traffic safety engineers in enhancing roundabout safety predictions, particularly in small-scale models where traditional methods may be impractical. Full article

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22 pages, 8948 KiB

Open AccessArticle

Electromechanical Impedance-Based Compressive Load-Induced Damage Identification of Fiber-Reinforced Concrete

by George M. Sapidis, Maria C. Naoum and Nikos A. Papadopoulos

Infrastructures 2025, 10(3), 60; https://doi.org/10.3390/infrastructures10030060 - 10 Mar 2025

Abstract

Establishing dependable and resilient methodologies for identifying damage that may compromise the integrity of reinforced concrete (RC) infrastructures is imperative for preventing potential catastrophic failures. Continuous evaluation and Structural Health Monitoring (SHM) can play a key role in extending the lifespan of new [...] Read more.

Establishing dependable and resilient methodologies for identifying damage that may compromise the integrity of reinforced concrete (RC) infrastructures is imperative for preventing potential catastrophic failures. Continuous evaluation and Structural Health Monitoring (SHM) can play a key role in extending the lifespan of new or existing buildings. At the same time, early crack detection in critical members prevents bearing capacity loss and potential failures, enhancing safety and reliability. Furthermore, implementing discrete fibers in concrete has significantly improved the ductility and durability of Fiber-Reinforced Concrete (FRC). The present study employs a hierarchical clustering analysis (HCA) to identify damage in FRC by analyzing the raw Electromechanical Impedance (EMI) signature of piezoelectric lead zirconate titanate (PZT) transducers. The experimental program consisted of three FRC standard cylinders subjected to repeated loading. The loading procedure consists of 6 incremental steps carefully selected to gradually deteriorate FRC’s structural integrity. Additionally, three PZT patches were adhered across the height of its specimen using epoxy resin, and their EMI response was captured between each loading step. Subsequently, the HCA was conducted for each PZT transducer individually. The experimental investigation demonstrates the efficacy of HCA in detecting load-induced damage in FRC through the variations in the EMI signatures of externally bonded PZT sensors. Full article

(This article belongs to the Special Issue Structural Health Monitoring, Non-destructive Evaluation and Remedial Measures for Civil Infrastructures)

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8 pages, 172 KiB

Open AccessEditorial

Innovative Solutions for Concrete Applications

by Patricia Kara De Maeijer

Infrastructures 2025, 10(3), 59; https://doi.org/10.3390/infrastructures10030059 - 10 Mar 2025

Abstract

Concrete, having evolved over the last 2000 years, is integral to modern infrastructure, with continuous innovations aiming to address sustainability challenges. From Roman concrete mixes to the invention of Portland cement (PC), concrete has evolved to meet growing infrastructure demands. As urbanization and [...] Read more.

Concrete, having evolved over the last 2000 years, is integral to modern infrastructure, with continuous innovations aiming to address sustainability challenges. From Roman concrete mixes to the invention of Portland cement (PC), concrete has evolved to meet growing infrastructure demands. As urbanization and energy consumption increase, the construction industry is focusing on high-performance materials, recycling, and minimizing harmful substances. Research on sustainable concrete alternatives shows promising reductions in global warming potential and other environmental impacts compared to traditional PC. However, challenges such as higher material costs and performance limitations remain. Alternatives such as alkali-activated concrete (AAC), self-healing concrete, and bacterial concrete (BC) have emerged in response to environmental concerns, along with fiber-reinforced AAC, waste-based concrete composites, and the reuse of construction and demolition waste (CDW), further enhancing sustainability. Foamed concrete, with its lightweight and insulating properties, offers additional potential for reducing environmental impact due to its ability to incorporate recycled materials and reduce raw material consumption. Technologies like three-dimensional concrete printing (3DCP) are improving resource efficiency and reducing carbon footprints while also lowering labor and material waste. However, concerns regarding cost-effectiveness and social sustainability persist. Overall, continued innovation is the key to balancing performance, cost, and sustainability in the development of concrete and to meet the growing demands of global infrastructure. Full article

(This article belongs to the Special Issue Innovative Solutions for Concrete Applications)

17 pages, 9622 KiB

Open AccessArticle

A Study on the Direct Application of the Gaussian Kernel Smoothing Filter for Bridge Health Monitoring

by Hadi Kordestani and Ehsan Pegah

Infrastructures 2025, 10(3), 58; https://doi.org/10.3390/infrastructures10030058 - 10 Mar 2025

Abstract

In this paper, the application of the Gaussian Kernel Smoothing Filter (GKSF) in the field of structural health monitoring (SHM) for bridges is explored. A baseline-free, GKSF-based method is developed to detect and localize structural damage in bridges subjected to truckloads. The study [...] Read more.

In this paper, the application of the Gaussian Kernel Smoothing Filter (GKSF) in the field of structural health monitoring (SHM) for bridges is explored. A baseline-free, GKSF-based method is developed to detect and localize structural damage in bridges subjected to truckloads. The study reveals that an adjusted GKSF can effectively smooth acceleration responses, where the smoothed response is dominated by the first natural frequency of the bridge. By employing a damage index (DI) based on the normalized energy of the smoothed acceleration signal, the method successfully identifies both the location and severity of structural damage in bridge structures. To validate the proposed approach, a simply supported bridge under a moving sprung mass is numerically modeled, and acceleration responses are obtained along the bridge’s length. The results indicate that the method is capable of accurately identifying the location and severity of structural damage, even in noisy environments. Notably, since the method does not require the determination or monitoring of dynamic modal parameters, it is classified as a baseline-free and rapid damage detection technique. Full article

(This article belongs to the Special Issue Structural Health Monitoring in Bridge Engineering)

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29 pages, 1565 KiB

Open AccessArticle

Analyzing High-Speed Rail’s Transformative Impact on Public Transport in Thailand Using Machine Learning

by Chinnakrit Banyong, Natthaporn Hantanong, Panuwat Wisutwattanasak, Thanapong Champahom, Kestsirin Theerathitichaipa, Rattanaporn Kasemsri, Manlika Seefong, Vatanavongs Ratanavaraha and Sajjakaj Jomnonkwao

Infrastructures 2025, 10(3), 57; https://doi.org/10.3390/infrastructures10030057 - 10 Mar 2025

Abstract

This study investigates the impact of high-speed rail (HSR) on Thailand’s public transportation market and evaluates the effectiveness of machine learning techniques in predicting travel mode choices. A stated preference survey was conducted with 3200 respondents across 16 provinces, simulating travel scenarios involving [...] Read more.

This study investigates the impact of high-speed rail (HSR) on Thailand’s public transportation market and evaluates the effectiveness of machine learning techniques in predicting travel mode choices. A stated preference survey was conducted with 3200 respondents across 16 provinces, simulating travel scenarios involving buses, trains, airplanes, and HSR. The dataset, consisting of 38,400 observations, was analyzed using the CatBoost model and the multinomial logit (MNL) model. CatBoost demonstrated superior predictive performance, achieving an accuracy of 0.853 and an AUC of 0.948, compared to MNL’s accuracy of 0.749 and AUC of 0.879. Shapley additive explanations (SHAP) analysis identified key factors influencing travel behavior, including cost, service frequency, waiting time, travel time, and station access time. The results predict that HSR will capture 88.91% of the intercity travel market, significantly reducing market shares for buses (4.76%), trains (5.11%), and airplanes (1.22%). The findings highlight the transformative role of HSR in reshaping travel patterns and offer policy insights for optimizing pricing, service frequency, and accessibility. Machine learning enhances predictive accuracy and enables a deeper understanding of mode choice behavior, providing a robust analytical framework for transportation planning. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence for Infrastructures)

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20 pages, 9066 KiB

Open AccessArticle

Evaluation of Performance of Repairs in Post-Tensioned Box Girder Bridge via Live Load Test and Acoustic Emission Monitoring

by Hang Zeng, Julie Ann Hartell and Robert Emerson

Infrastructures 2025, 10(3), 56; https://doi.org/10.3390/infrastructures10030056 - 9 Mar 2025

Abstract

In this paper, bridge live load testing was conducted to examine the performance of repairs on a section of a post-tensioned box girder bridge in Oklahoma City, Oklahoma. The live load test was performed with a single/group of truck(s) with known gross weight. [...] Read more.

In this paper, bridge live load testing was conducted to examine the performance of repairs on a section of a post-tensioned box girder bridge in Oklahoma City, Oklahoma. The live load test was performed with a single/group of truck(s) with known gross weight. The objective of this study was to characterize the behavior of the test bridge span by comparing the performance of a repair in situ as part of the bridge section’s structural response to that of a section known to be sound. To achieve the objective, the structural strain response was collected from several critical locations across the bridge girders. A comparative analysis of bridge behavior was carried out for the results from both the repaired and structurally sound areas to identify any deterioration and adverse changes. The structural strain response indicated an elastic behavior of the tested bridge span under three different load levels. Meanwhile, acoustic emission monitoring was implemented as a supplementary evaluation method. The acoustic emission intensity analysis also revealed an insignificant change in the effectiveness of the repair upon comparing results obtained from both locations. Although there were fluctuations in the b-value, it consistently remained above one across the different load testing scenarios, indicating no progressive damage and generally reflecting structural soundness, aligning with the absence of visible cracks in the monitored area. Full article

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

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27 pages, 5454 KiB

Open AccessArticle

An MCDM Approach to Lean Tool Implementation for Minimizing Non-Value-Added Activities in the Precast Industry

by Haritha Malika Dara, Musa Adamu, Prachi Vinod Ingle, Ashwin Raut and Yasser E. Ibrahim

Infrastructures 2025, 10(3), 55; https://doi.org/10.3390/infrastructures10030055 - 6 Mar 2025

Abstract

The construction industry is growing with the shortfall issues of productivity, functionality, and cost. Precast construction has significant potential to address these issues by incorporating lean principles. Lean focuses on enhancing value at every stage of the construction process. By combining these two [...] Read more.

The construction industry is growing with the shortfall issues of productivity, functionality, and cost. Precast construction has significant potential to address these issues by incorporating lean principles. Lean focuses on enhancing value at every stage of the construction process. By combining these two approaches, the construction industry can effectively tackle these challenges. This research aims to achieve two main objectives: (1). To establish a connection between lean tools and non-value added (NVA) activities, (2). To prioritize these lean tools based on their relevance to major NVA activities. To accomplish this, an extensive review of the literature was conducted to examine the adoption of lean tools in various NVA tasks. A questionnaire survey was then employed to identify the root causes of NVA activities (criteria) and determine the most suitable lean tools for addressing each specific criterion. The findings from multi-criteria decision decision-making (MCDM) analysis highlight that total quality management (TQM) is ranked first in two methods while continuous improvement (CI) ranked first in one method. Comparing all the scenarios, it is observed that 5S and CI have been fluctuating between two and three rankings, and the remaining ranks have very minute changes. Based on all these lean tools are prioritized as TQM > CI > 5S > JIT > VSM > PY. Full article

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