Infrastructures

22 pages, 703 KiB

Open AccessArticle

An Impact Assessment of Speed Humps’ Geometric Characteristics and Spacing on Vehicle Speed: An Overview

by Nawaf M. Alshabibi

Infrastructures 2025, 10(7), 190; https://doi.org/10.3390/infrastructures10070190 - 21 Jul 2025

Viewed by 291

This review examines the effect of geometric properties and the spacing of road humps on vehicle speed and noise, with a particular emphasis on South Asian contexts, especially Malaysia. Road humps are widely used traffic-calming devices designed to reduce vehicle speed and enhance [...] Read more.

This review examines the effect of geometric properties and the spacing of road humps on vehicle speed and noise, with a particular emphasis on South Asian contexts, especially Malaysia. Road humps are widely used traffic-calming devices designed to reduce vehicle speed and enhance road safety. The effectiveness of these measures is strongly influenced by parameters such as height, width, profile, and placement intervals. While the geometric optimization of humps generally improves speed-reduction outcomes, several studies indicate that braking and acceleration at humps can lead to increased traffic noise, particularly in residential and high-density areas. This review also explores design strategies and material choices (e.g., asphalt use, sinusoidal profiles) that may help mitigate noise impacts. Overall, a balance between speed control and noise management is necessary to ensure both safety and community acceptance. Full article

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

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19 pages, 3048 KiB

Open AccessArticle

Machine Learning-Based Highway Pavement Performance Prediction in Xinjiang

by Qi Yang, Wei Tian and Xiaomin Dai

Infrastructures 2025, 10(7), 189; https://doi.org/10.3390/infrastructures10070189 - 21 Jul 2025

Viewed by 243

Abstract

Assessing highway pavement condition is crucial for ensuring transportation safety and optimizing infrastructure maintenance. In Xinjiang, China, extreme climatic and traffic conditions pose significant challenges to pavement performance. This study introduces a machine-learning-based framework to predict asphalt pavement performance in Xinjiang. We integrate [...] Read more.

Assessing highway pavement condition is crucial for ensuring transportation safety and optimizing infrastructure maintenance. In Xinjiang, China, extreme climatic and traffic conditions pose significant challenges to pavement performance. This study introduces a machine-learning-based framework to predict asphalt pavement performance in Xinjiang. We integrate various factors (design, materials, environment, traffic, and maintenance) into regression models, creating a region-specific pavement performance decay model. Our data preprocessing methodology effectively addresses outliers and missing data, ensuring the model’s robustness. The findings offer insights into asphalt pavement behavior in Xinjiang and provide guidance for maintenance strategies. The proposed model enhances highway infrastructure safety and cost-effectiveness. Future research will focus on refining the model with more data and exploring complex variable interactions. Full article

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17 pages, 9414 KiB

Open AccessArticle

Influence of High-Speed Flow on Aerodynamic Lift of Pantograph at 400 km/h

by Zhao Xu, Hongwei Zhang, Wen Wang and Guobin Lin

Infrastructures 2025, 10(7), 188; https://doi.org/10.3390/infrastructures10070188 - 17 Jul 2025

Viewed by 240

Abstract

This study examines pantograph aerodynamic lift at 400 km/h, and uncovers the dynamic behaviors and mechanisms that influence pantograph–catenary performance. Using computational fluid dynamics (CFD) with a compressible fluid model and an SST k-ω turbulence model, aerodynamic characteristics were analyzed. Simulation data at [...] Read more.

This study examines pantograph aerodynamic lift at 400 km/h, and uncovers the dynamic behaviors and mechanisms that influence pantograph–catenary performance. Using computational fluid dynamics (CFD) with a compressible fluid model and an SST k-ω turbulence model, aerodynamic characteristics were analyzed. Simulation data at 300, 350, and 400 km/h showed lift fluctuation amplitude increases with speed, peaking near 50 N at 400 km/h. Power spectral density (PSD) energy, dominated by low frequencies, peaked around 10 dB/Hz in the low-frequency band, highlighting exacerbated lift instability. Component analysis revealed the smallest lift-to-drag ratio and most significant fluctuations at the head, primarily due to boundary-layer separation and vortex shedding from its non-streamlined design. Turbulence energy analysis identified the head and base as main turbulence sources; however, base vibrations are absorbed by the vehicle body, while the head causes pantograph–catenary vibrations due to direct contact. These findings confirm that aerodynamic instability at the head is the main cause of contact force fluctuations. Optimizing head design is necessary to suppress fluctuations, ensuring safe operation at 400 km/h and above. Results provide a theoretical foundation for aerodynamic optimization and improved dynamic performance of high-speed pantographs. Full article

(This article belongs to the Special Issue The Resilience of Railway Networks: Enhancing Safety and Robustness)

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23 pages, 5255 KiB

Open AccessArticle

Modeling and Classification of Random Traffic Patterns for Fatigue Analysis of Highway Bridges

by Xianglong Zheng, Bin Chen, Zhicheng Zhang, He Zhang, Jing Liu and Jingyao Zhang

Infrastructures 2025, 10(7), 187; https://doi.org/10.3390/infrastructures10070187 - 17 Jul 2025

Viewed by 270

Abstract

With the increasing severity of traffic congestion, the impact of random traffic patterns has emerged as an indispensable factor in the fatigue design and assessment of highway bridges. In this study, an analytical approach has been proposed for modeling the effects of random [...] Read more.

With the increasing severity of traffic congestion, the impact of random traffic patterns has emerged as an indispensable factor in the fatigue design and assessment of highway bridges. In this study, an analytical approach has been proposed for modeling the effects of random traffic patterns on fatigue damage. A fatigue damage ratio, referred to as RPEF, is introduced to establish the correlation between damage and traffic characteristics. Two quantitative parameters representing two characteristics of traffic loads, namely the average loading occurrence number (scale parameter) and the vehicle headway COVs (shape parameter), have been found to be excellent indices for clustering the different dimensional randomness of RPEFs. Based on a comprehensive case study, the realization of using equivalent RPEFs to evaluate bridge fatigue damage under mixed traffic conditions was explored. The results indicate that the actual fatigue damage of bridges can be evaluated through the superposition of different traffic pattern effects, provided that the pattern classification number fits the fluctuations in traffic flow. It is necessary to ensure the rationality of traffic pattern classification for structures with spans greater than 50 m, as an overly simplistic traffic pattern classification may lead to an underestimation of fatigue damage. Full article

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

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26 pages, 11962 KiB

Open AccessArticle

A Microsimulation-Based Methodology for Evaluating Efficiency and Safety in Roundabout Corridors: Case Studies of Pisa (Italy) and Avignon (France)

by Lorenzo Brocchini, Antonio Pratelli, Didier Josselin and Massimo Losa

Infrastructures 2025, 10(7), 186; https://doi.org/10.3390/infrastructures10070186 - 17 Jul 2025

Viewed by 315

Abstract

This research is part of a broader investigation into innovative simulation-based approaches for improving traffic efficiency and road safety in roundabout corridors. These corridors, composed of successive roundabouts along arterials, present systemic challenges due to the dynamic interactions between adjacent intersections. While previous [...] Read more.

This research is part of a broader investigation into innovative simulation-based approaches for improving traffic efficiency and road safety in roundabout corridors. These corridors, composed of successive roundabouts along arterials, present systemic challenges due to the dynamic interactions between adjacent intersections. While previous studies have addressed localized inefficiencies or proposed isolated interventions, this paper introduces possible replicable methodology based on a microsimulation and surrogate safety analysis to evaluate roundabout corridors as integrated systems. In this context, efficiency refers to the ability of a road corridor to maintain stable traffic conditions under a given demand scenario, with low delay times corresponding to acceptable levels of service. Safety is interpreted as the minimization of vehicle conflicts and critical interactions, evaluated through surrogate measures derived from simulated vehicle trajectories. The proposed approach—implemented through Aimsun Next and the SSAM tool—is tested on two real-world corridors: Via Aurelia Nord in Pisa (Italy) and Route de Marseille in Avignon (France), assessing multiple intersection configurations that combine roundabouts and signal-controlled junctions. Results show how certain layouts can produce unexpected performance outcomes, underlining the importance of system-wide evaluations. The proposed framework aims to support engineers and planners in identifying optimal corridor configurations under realistic operating conditions. Full article

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

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24 pages, 10779 KiB

Open AccessArticle

Digital Measurement Method for Main Arch Rib of Concrete-Filled Steel Tube Arch Bridge Based on Laser Point Cloud

by Zhiguan Huang, Chuanli Kang, Junli Liu and Hongjian Zhou

Infrastructures 2025, 10(7), 185; https://doi.org/10.3390/infrastructures10070185 - 12 Jul 2025

Viewed by 224

Abstract

Aiming to address the problem of low efficiency in the traditional manual measurement of the main arch rib components of concrete-filled steel tube (CFST) arch bridges, this study proposes a digital measurement technology based on the integration of geometric parameters and computer-aided design [...] Read more.

Aiming to address the problem of low efficiency in the traditional manual measurement of the main arch rib components of concrete-filled steel tube (CFST) arch bridges, this study proposes a digital measurement technology based on the integration of geometric parameters and computer-aided design (CAD) models. In this method, first, we perform the high-precision registration of the preprocessed scanned point cloud of the CFST arch rib components with the discretized design point cloud of the standardized CAD model. Then, in view of the fact that the fitting of point cloud geometric parameters is susceptible to the influence of sparse or uneven massive point clouds, these points are treated as outliers for elimination. We propose a method incorporating slicing to solve the interference of outliers and improve the fitting accuracy. Finally, the evaluation of quality, accuracy, and efficiency is carried out based on distance deviation analysis and geometric parameter comparison. The experimental results show that, for the experimental data, the fitting error of this method is reduced by 76.32% compared with the traditional method, which can improve the problems with measurement and fitting seen with the traditional method. At the same time, the measurement efficiency is increased by 5% compared with the traditional manual method. Full article

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42 pages, 5471 KiB

Open AccessArticle

Optimising Cyclist Road-Safety Scenarios Through Angle-of-View Analysis Using Buffer and GIS Mapping Techniques

by Zahra Yaghoobloo, Giuseppina Pappalardo and Michele Mangiameli

Infrastructures 2025, 10(7), 184; https://doi.org/10.3390/infrastructures10070184 - 11 Jul 2025

Viewed by 240

Abstract

In the present era, achieving sustainability requires the development of planning strategies to develop a safer urban infrastructure. This study examines the realistic aspects of cyclist safety by analysing cyclists’ fields of view, using Geographic Information Systems (GIS) and spatial data analysis. The [...] Read more.

In the present era, achieving sustainability requires the development of planning strategies to develop a safer urban infrastructure. This study examines the realistic aspects of cyclist safety by analysing cyclists’ fields of view, using Geographic Information Systems (GIS) and spatial data analysis. The research introduces novel geoprocessing tools-based GIS techniques that mathematically simulate cyclists’ angles of view and the distances to nearby environmental features. It provides precise insights into some potential hazards and infrastructure challenges encountered while cycling. This research focuses on managing and analysing the data collected, utilising OpenStreetMap (OSM) as vector-based supporting data. It integrates cyclists’ behavioural data with the urban environmental features encountered, such as intersections, road design, and traffic controls. The analysis is categorised into specific classes to evaluate the impacts of these aspects of the environment on cyclists’ behaviours. The current investigation highlights the importance of integrating the objective environmental elements surrounding the route with subjective perceptions and then determining the influence of these environmental elements on cyclists’ behaviours. Unlike previous studies that ignore cyclists’ visual perspectives in the context of real-world data, this work integrates objective GIS data with cyclists’ field of view-based modelling to identify high-risk areas and highlight the need for enhanced safety measures. The proposed approach equips urban planners and designers with data-informed strategies for creating safer cycling infrastructure, fostering sustainable mobility, and mitigating urban congestion. 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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22 pages, 7152 KiB

Open AccessArticle

Comprehensive Substantiation of the Impact of Pre-Support Technology on a 50-Year-Old Subway Station During the Construction of Undercrossing Tunnel Lines

by Bin Zhang, Shaohui He, Jianfei Ma, Jiaxin He, Yiming Li and Jinlei Zheng

Infrastructures 2025, 10(7), 183; https://doi.org/10.3390/infrastructures10070183 - 11 Jul 2025

Viewed by 178

Abstract

Due to the long operation period of Beijing Metro Line 2 and the complex surrounding building environment, this paper comprehensively studied the mechanical properties of new tunnels using close-fitting undercrossing based on pre-support technology. To control structural deformation caused by the expansion project, [...] Read more.

Due to the long operation period of Beijing Metro Line 2 and the complex surrounding building environment, this paper comprehensively studied the mechanical properties of new tunnels using close-fitting undercrossing based on pre-support technology. To control structural deformation caused by the expansion project, methods such as laboratory tests, numerical simulation, and field tests were adopted to systematically analyze the tunnel mechanics during the undercrossing of existing metro lines. First, field tests were carried out on the existing Line 2 and Line 3 tunnels during the construction period. It was found that the close-fitting construction based on pre-support technology caused small deformation displacement in the subway tunnels, with little impact on the smoothness of the existing subway rail surface. The fluctuation range was −1 to 1 mm, ensuring the safety of existing subway operations. Then, a refined finite difference model for the close-fitting undercrossing construction process based on pre-support technology was established, and a series of field and laboratory tests were conducted to obtain calculation parameters. The reliability of the numerical model was verified by comparing the monitored deformation of existing structures with the simulated structural forces and deformations. The influence of construction methods on the settlement changes of existing line tracks, structures, and deformation joints was discussed. The research results show that this construction method effectively controls the settlement deformation of existing lines. The settlement deformation of existing lines is controlled within 1~3 cm. The deformation stress of the existing lines is within the concrete strength range of the existing structure, and the tensile stress is less than 3 MPa. The maximum settlement and maximum tensile stress of the station in the pre-support jacking scheme are −5.27 mm and 2.29 MPa. The construction scheme with pre-support can more significantly control structural deformation, reduce stress variations in existing line structures, and minimize damage to concrete structures. Based on the monitoring data and simulation results, some optimization measures were proposed. Full article

(This article belongs to the Special Issue Recent Advances in Railway Engineering)

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14 pages, 1459 KiB

Open AccessArticle

Research on the Dynamic Response of the Catenary of the Co-Located Railway for Conventional/High Speed Trains in High-Wind Area

by Guanghui Li, Yongzhi Gou, Binqian Guo, Hongmei Li, Enfan Cao and Junjie Ma

Infrastructures 2025, 10(7), 182; https://doi.org/10.3390/infrastructures10070182 - 11 Jul 2025

Viewed by 221

Abstract

To establish a theoretical foundation for assessing the dynamic performance of high-speed train catenary systems in wind-prone regions, this study develops a coupled pantograph–catenary model using ANSYS(2022R1) APDL. The dynamic responses of conventional high-speed pantographs traversing both mainline and transition sections are analyzed [...] Read more.

To establish a theoretical foundation for assessing the dynamic performance of high-speed train catenary systems in wind-prone regions, this study develops a coupled pantograph–catenary model using ANSYS(2022R1) APDL. The dynamic responses of conventional high-speed pantographs traversing both mainline and transition sections are analyzed under varying operational conditions. The key findings reveal that an elevated rated tension in the contact wire and messenger wire reduces the pantograph lift in wind areas with no crosswind compared to non-wind areas, with an average lift reduction of 8.52% and diminished standard deviation, indicating enhanced system stability. Under a 20 m/s crosswind, both tested pantograph designs maintain contact force and dynamic lift within permissible thresholds, while significant catenary undulations predominantly occur at mid-span locations. Active control strategies preserve the static lift force but induce pantograph flattening under compression, reducing aerodynamic drag and resulting in smaller contact force fluctuations relative to normal-speed sections. In contrast, passive control increases static lift, thereby causing greater fluctuations in contact force compared to baseline conditions. The superior performance of active control is attributed to its avoidance of static lift amplification, which dominates the dynamic response in passive systems. Full article

(This article belongs to the Special Issue The Resilience of Railway Networks: Enhancing Safety and Robustness)

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13 pages, 4275 KiB

Open AccessArticle

Integrating Recycled Acrylonitrile–Butadiene–Styrene Plastics from Electronic Waste with Carbon Black for Sustainable Asphalt Production

by Sepehr Mohammadi, Dongzhao Jin and Zhanping You

Infrastructures 2025, 10(7), 181; https://doi.org/10.3390/infrastructures10070181 - 11 Jul 2025

Viewed by 303

Abstract

As the global demand for electronic equipment continues to grow, many devices are being replaced more frequently, resulting in a rapid rise in electronic waste (e-waste), now the fastest growing waste stream worldwide. Motivated by this, the objective of this study is to [...] Read more.

As the global demand for electronic equipment continues to grow, many devices are being replaced more frequently, resulting in a rapid rise in electronic waste (e-waste), now the fastest growing waste stream worldwide. Motivated by this, the objective of this study is to present an environmentally friendly method to recycle acrylonitrile–butadiene–styrene (ABS), one of the most common e-waste plastics, by using it for asphalt production. In contrast to earlier methods of plastic-modified asphalt production involving complex pretreatments or complimentary additives unsuitable for plant-scale use, this study aims to demonstrate a practical, low-cost solution through the use of carbon black. This approach included physically pretreating ABS plastics for size reduction and incorporating waste tire-derived carbon black to promote effective dispersion in asphalt during wet modification. The rheological properties of the e-waste-modified asphalt were subsequently assessed. The test results indicated that recycling ABS plastics with a blending content of 5% alongside 5% carbon black can enhance cold-weather cracking resistance and high-temperature anti-rutting performance of asphalt. The enhancement can be attributed to the proper preparation procedures of ABS plastics and the addition of carbon black, which can further improve the performance by promoting the proper dispersion of plastic particles in asphalt. The outcome of this study indicates that recycling e-waste plastics through asphalt production can lead to more green and sustainable asphalt construction, reduce total construction costs, and most importantly enhance performance. Full article

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14 pages, 6002 KiB

Open AccessTechnical Note

Railway Infrastructure Upgrade for Freight Transport: Case Study of the Røros Line, Norway

by Are Solheim, Gustav Carlsen Gjestad, Christoffer Østmoen, Ørjan Lydersen, Stefan Andreas Edin Nilsen, Diego Maria Barbieri and Baowen Lou

Infrastructures 2025, 10(7), 180; https://doi.org/10.3390/infrastructures10070180 - 10 Jul 2025

Viewed by 301

Abstract

Compared to road trucks, the use of trains to move goods along railway lines is a more sustainable freight transport system. In Norway, where several main lines are single tracks, the insufficient length of many of the existing passing loops considerably restricts the [...] Read more.

Compared to road trucks, the use of trains to move goods along railway lines is a more sustainable freight transport system. In Norway, where several main lines are single tracks, the insufficient length of many of the existing passing loops considerably restricts the operational and economic benefits of long trains. This brief technical note revolves around the possible upgrade of the Røros line connecting Oslo and Trondheim to accommodate 650 m-long freight trains as an alternative to the heavily trafficked Dovre line. Pivoting on regulatory standards, this exploratory work identifies the minimum set of infrastructure modifications required to achieve the necessary increase in capacity by extending the existing passing loops and creating a branch line. The results indicate that 8 freight train routes can be efficiently implemented, in addition to the 12 existing passenger train routes. This brief technical note employs building information modeling software Trimble Novapoint edition 2024 to position the existing railway infrastructure on topographic data and visualize the suggested upgrade. Notwithstanding the limitations of this exploratory work, dwelling on capacity calculation and the design of infrastructure upgrades, the results demonstrate that modest and well-placed interventions can significantly enhance the strategic value of a single-track rail corridor. This brief technical note sheds light on the main areas to be addressed by future studies to achieve a comprehensive evaluation of the infrastructure upgrade, also covering technical construction and economic aspects. Full article

(This article belongs to the Special Issue Sustainable Materials, Design, Maintenance, and Management for Transport Infrastructure)

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22 pages, 2047 KiB

Open AccessArticle

Structure Formation and Curing Stage of Arbolite–Concrete Composites Based on Iron-Sulfur Binders

by Baizak Isakulov, Abilkhair Issakulov and Agnieszka Dąbska

Infrastructures 2025, 10(7), 179; https://doi.org/10.3390/infrastructures10070179 - 10 Jul 2025

Viewed by 299

Abstract

The paper deals with the issue of obtaining iron-sulfur-containing binders through their mechanochemical treatment using mutual neutralization and detoxification structure formation, and the curing stage of arbolite concrete composites based on industrial waste under long-term loading were also studied. Due to abrasion and [...] Read more.

The paper deals with the issue of obtaining iron-sulfur-containing binders through their mechanochemical treatment using mutual neutralization and detoxification structure formation, and the curing stage of arbolite concrete composites based on industrial waste under long-term loading were also studied. Due to abrasion and impact, the mutual neutralization and detoxification methods of industrial waste toxic components through their mechanochemical treatment on the structures of ball mill LShM-750, were used to obtain iron-sulfur-containing binders. Pyrite cinders acted as oxidizing agents, and elementary technical sulfur had reduced properties. To determine the rate of creep strain growth, the load on prism samples was applied in the form of specially made spring units at stress levels of 0.15 R_bn, 0.44 R_bn, and 0.74 R_bn, where R_bn is the prism strength of iron-sulfur-containing arbolite concrete in compression. The strength and fracture formations of lightweight iron-sulfur concrete were studied using strain gauge apparatus and depth strain gauges glued on shredded reed fibers using adhesive, installed before concreting. It was revealed that the introduction of a sulfur additive within the range from 10 to 13% increases the compressive strength of iron-sulfur-containing concrete composites prepared with that of mortars at a water/solid ratio equal to 0.385 in wet and dry states. It is found that the deformations occurring under applied load growth proportionally to it, and deviation from this regularity was observed for lightweight iron-sulfur-containing concrete only at high compressive stresses. It was also proved that the destruction of iron-sulfur-containing arbolite occurs sequentially. First, the destruction of the mortar component is observed, and then the organic aggregate in the form of crushed reed fiber is destroyed. It was confirmed that arbolite concrete composite can be used as an effective wall material for civil engineering structure, especially in seismic regions of Kazakhstan. Full article

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32 pages, 2007 KiB

Open AccessArticle

Exploring the Relationship Between Project Characteristics and Time–Cost Deviations for Colombian Rural Roads

by Jose Quintero, Alexander Murgas, Adriana Gómez-Cabrera and Omar Sánchez

Infrastructures 2025, 10(7), 178; https://doi.org/10.3390/infrastructures10070178 - 9 Jul 2025

Viewed by 553

Abstract

Rural road programs are essential for enhancing connectivity in remote areas, yet they frequently encounter schedule delays and budget overruns. This study explores the extent to which specific project characteristics influence these deviations in Colombian rural road contracts. A dataset comprising 229 projects [...] Read more.

Rural road programs are essential for enhancing connectivity in remote areas, yet they frequently encounter schedule delays and budget overruns. This study explores the extent to which specific project characteristics influence these deviations in Colombian rural road contracts. A dataset comprising 229 projects was extracted from the national SECOP open-procurement platform and processed using the CRISP-DM protocol. Following the cleaning and coding of 14 project-level variables, statistical analyses were conducted using Spearman correlations, Kruskal–Wallis tests, and post-hoc Wilcoxon comparisons to identify significant bivariate relations I confirm I confirm I confirm hips. A Random Forest model was subsequently applied to determine the most influential multivariate predictors of cost and time deviations. In parallel, a directed content analysis of contract addenda reclassified 22 recorded deviation descriptors into ten internationally recognized categories of causality, enabling an integrated interpretation of both statistical and documentary evidence. The findings indicate that contract value, geographical region, and contractor configuration are significant determinants of cost and time performance. Additionally, project intensity and discrepancies between awarded and bid values emerged as key contributors to cost escalation. Scope changes and adverse weather conditions together accounted for 76% of all documented deviation triggers, underscoring the relevance of robust front-end planning and climate-risk considerations in rural infrastructure delivery. The findings provide information for stakeholders, policymakers, and professionals who aim to manage the risk of schedule and budget deviations in public infrastructure projects. Full article

(This article belongs to the Special Issue Sustainable Materials, Design, Maintenance, and Management for Transport Infrastructure)

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27 pages, 7944 KiB

Open AccessArticle

Graphical Empirical Mode Decomposition–Convolutional Neural Network-Based Expert System for Early Corrosion Detection in Truss-Type Bridges

by Alan G. Lujan-Olalde, Angel H. Rangel-Rodriguez, Andrea V. Perez-Sanchez, Martin Valtierra-Rodriguez, Jose M. Machorro-Lopez and Juan P. Amezquita-Sanchez

Infrastructures 2025, 10(7), 177; https://doi.org/10.3390/infrastructures10070177 - 8 Jul 2025

Viewed by 227

Abstract

Corrosion is a critical issue in civil structures, significantly affecting their durability and functionality. Detecting corrosion at an early stage is essential to prevent structural failures and ensure safety. This study proposes an expert system based on a novel methodology for corrosion detection [...] Read more.

Corrosion is a critical issue in civil structures, significantly affecting their durability and functionality. Detecting corrosion at an early stage is essential to prevent structural failures and ensure safety. This study proposes an expert system based on a novel methodology for corrosion detection using vibration signal analysis. The approach employs graphical empirical mode decomposition (GEMD) to decompose vibration signals into their intrinsic mode functions, extracting relevant structural features. These features are then transformed into grayscale images and classified using a Convolutional Neural Network (CNN) to automatically differentiate between a healthy structure and one affected by corrosion. To enhance the computational efficiency of the method without compromising accuracy, different CNN architectures and image sizes are tested to propose a low-complexity model. The proposed approach is validated using a 3D nine-bay truss-type bridge model encountered in the Vibrations Laboratory at the Autonomous University of Querétaro, Mexico. The evaluation considers three different corrosion levels: (1) incipient, (2) moderate, and (3) severe, along with a healthy condition. The combination of GEMD and CNN provides a highly accurate corrosion detection framework that achieves 100% classification accuracy while remaining effective regardless of the damage location and severity, making it a reliable tool for early-stage corrosion assessment that enables timely maintenance and enhances structural health monitoring to improve the long life and safety of civil structures. Full article

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

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33 pages, 11163 KiB

Open AccessArticle

3D Modular Construction Made of Precast SFRC-Stiffened Panels

by Sannem Ahmed Salim Landry Sawadogo, Tan-Trung Bui, Abdelkrim Bennani, Dhafar Al Galib, Pascal Reynaud and Ali Limam

Infrastructures 2025, 10(7), 176; https://doi.org/10.3390/infrastructures10070176 - 7 Jul 2025

Viewed by 386

Abstract

A new concept of a 3D volumetric module, made up of six plane stiffened self-compacting fiber-reinforced concrete (SFRC) panels, is here studied. Experimental campaigns are carried out on SFRC material and on the thin-slab structures used for this modular concept. The high volume [...] Read more.

A new concept of a 3D volumetric module, made up of six plane stiffened self-compacting fiber-reinforced concrete (SFRC) panels, is here studied. Experimental campaigns are carried out on SFRC material and on the thin-slab structures used for this modular concept. The high volume of steel fibers (80 kg/m³) used in the formulation of this concrete allow a positive strain hardening to be obtained in the post-cracking regime observed on the bending characterization tests. The high mechanical material characteristics, obtained both in tension and compression, allow a significant decrease in the module slabs’ thickness. The tests carried out on the 7 cm thick slab demonstrate a high load-bearing capacity and ductility under bending loading; this is also the case for shear loading configuration, although without any shear reinforcements. Numerical simulations of the material mechanical tests were conducted using Abaqus code; the results corroborate the experimental findings. Then, simulations were also conducted at the structural level, mainly to evaluate the behavior and the bearing capacity of the thin 3D module stiffened slabs. Finally, knowing that the concrete module truck transport can be a weak point, the decelerations induced during transportation were characterized and the integrity of the largest 3D module was demonstrated. Full article

(This article belongs to the Special Issue Seismic Performance Assessment of Precast Concrete)

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11 pages, 3937 KiB

Open AccessArticle

Dynamic Wheel Load Measurements by Optical Fiber Interferometry

by Daniel Kacik, Ivan Martincek and Peihong Cheng

Infrastructures 2025, 10(7), 175; https://doi.org/10.3390/infrastructures10070175 - 7 Jul 2025

Viewed by 188

Abstract

This study proposes a Fabry–Perot interferometric system and an associated evaluation method for measuring the weight of moving trains. An optical fiber sensor, comprising a sensing fiber and a supporting structure, is securely bonded to the rail foot. As a train traverses the [...] Read more.

This study proposes a Fabry–Perot interferometric system and an associated evaluation method for measuring the weight of moving trains. An optical fiber sensor, comprising a sensing fiber and a supporting structure, is securely bonded to the rail foot. As a train traverses the track, the resulting localized bending induces a change in the sensing fiber’s length, which manifests as a quantifiable phase shift in the interference signal. We developed a physical–mathematical model, based on three Gaussian functions, to describe the temporal change in sensing fiber length caused by the passage of a single bogie. This model enables the determination of a proportionality constant to accurately convert the measured phase change into train weight. Model validation was performed using a train set, including a locomotive and four variably loaded wagons, traveling at 15.47 km/h. This system offers a novel and effective approach for real-time train weight monitoring. Full article

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

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18 pages, 4458 KiB

Open AccessArticle

Intelligent Hybrid SHM-NDT Approach for Structural Assessment of Metal Components

by Romaine Byfield, Ahmed Shabaka, Milton Molina Vargas and Ibrahim Tansel

Infrastructures 2025, 10(7), 174; https://doi.org/10.3390/infrastructures10070174 - 6 Jul 2025

Viewed by 347

Abstract

Structural health monitoring (SHM) plays a pivotal role in ensuring the integrity and safety of critical infrastructure and mechanical components. While traditional non-destructive testing (NDT) methods offer high-resolution data, they typically require periodic access and disassembly of equipment to conduct inspections. In contrast, [...] Read more.

Structural health monitoring (SHM) plays a pivotal role in ensuring the integrity and safety of critical infrastructure and mechanical components. While traditional non-destructive testing (NDT) methods offer high-resolution data, they typically require periodic access and disassembly of equipment to conduct inspections. In contrast, SHM employs permanently installed, cost-effective sensors to enable continuous monitoring, though often with reduced detail. This study presents an integrated hybrid SHM-NDT methodology enhanced by deep learning to enable the real-time monitoring and classification of mechanical stresses in structural components. As a case study, a 6-foot-long parallel flange I-beam, representing bridge truss elements, was subjected to variable bending loads to simulate operational conditions. The hybrid system utilized an ultrasonic transducer (NDT) for excitation and piezoelectric sensors (SHM) for signal acquisition. Signal data were analyzed using 1D and 2D convolutional neural networks (CNNs), long short-term memory (LSTM) models, and random forest classifiers to detect and classify load magnitudes. The AI-enhanced approach achieved 100% accuracy in 47 out of 48 tests and 94% in the remaining tests. These results demonstrate that the hybrid SHM-NDT framework, combined with machine learning, offers a powerful and adaptable solution for continuous monitoring and precise damage assessment of structural systems, significantly advancing maintenance practices and safety assurance. 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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20 pages, 16120 KiB

Open AccessArticle

Lateral Performance of Steel–Concrete Anchors Embedded in RC Columns Subjected to Fire Scenario

by Amer Alkloub, Mahmoud Dwaikat, Ahmed Ashteyat, Farouq Sammour and Asala Jaradat

Infrastructures 2025, 10(7), 173; https://doi.org/10.3390/infrastructures10070173 - 5 Jul 2025

Viewed by 303

Abstract

The use of both structural steel and reinforced concrete is common in civil and military infrastructure projects. Anchorage plays a crucial role in these systems, serving as the key element that connects structural components and secures attachments within complex composite structures. This research [...] Read more.

The use of both structural steel and reinforced concrete is common in civil and military infrastructure projects. Anchorage plays a crucial role in these systems, serving as the key element that connects structural components and secures attachments within complex composite structures. This research focuses on evaluating the performance of steel–concrete column connections under the combined effects of lateral loading and fire exposure. Additionally, the study investigates the use of carbon fiber-reinforced polymers (CFRP) for strengthening and repairing these connections. The research methodology combines experimental testing and finite-element modeling to achieve its objectives. First, experimental investigation was carried out to test two groups of steel-reinforced concrete column specimens, each group made of three specimens. The first group specimens were designed based on special moment frame (SMF) detailing, and the other group specimens were designed based on intermediate moment frame (IMF) detailing. These two types of design were selected based on seismic demands, with SMFs offering high ductility and resilience for severe earthquakes and IMFs providing a cost-effective solution for moderate seismic zones, both benefiting from ongoing innovations in connection detailing and design approaches. Then, finite-element analysis was conducted to model the test specimens. High-fidelity finite-element modeling was conducted using ANSYS program, which included three-dimensional coupled thermal-stress analyses for the six tested specimens and incorporated nonlinear temperature-dependent materials characteristics of each component and the interfaces. Both the experimental and numerical results of this study show that fire has a more noticeable effect on displacement compared to the peak capacities of both types of specimens. Fire exposure results in a larger reduction in the initial residual lateral stiffness of the SMF specimens when compared to IMF specimens. While the effect of CFRP wraps on initial residual lateral stiffness was consistent for all specimens, it caused more improvement for the IMF specimen in terms of post-fire ductility when compared to SMF specimens. This exploratory study confirms the need for further research on the effect of fire on the concrete–steel anchorage zones. Full article

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19 pages, 6021 KiB

Open AccessArticle

Hyperspectral Signatures for Detecting the Concrete Hydration Process Using Neural Networks

by Shiming Li, Alfred Strauss, Damjan Grba, Maximilian Granzner, Benjamin Täubling-Fruleux and Thomas Zimmermann

Infrastructures 2025, 10(7), 172; https://doi.org/10.3390/infrastructures10070172 - 4 Jul 2025

Viewed by 263

Abstract

The curing process of a concrete sample has a significant influence on hydration and its strength. This means that inadequate curing conditions lead to a loss of concrete quality and negative consequences in structural engineering. In addition, different state-of-the-art (SOTA) curing surface treatments [...] Read more.

The curing process of a concrete sample has a significant influence on hydration and its strength. This means that inadequate curing conditions lead to a loss of concrete quality and negative consequences in structural engineering. In addition, different state-of-the-art (SOTA) curing surface treatments and hydration periods have a significant effect on durability. This paper introduces an innovative non-destructive method to detect the development of the hydration process under different treatment conditions. Hyperspectral imaging is a non-contact measurement technique that provides detailed information on hydration characteristics within an electromagnetic wavelength range. A comparative laboratory measurement was conducted on twelve concrete samples, subjected to three curing treatments and four curing surface treatments, over a hydration period from the 1st to the 56th day. Additionally, artificial neural networks and convolutional neural networks have achieved classification accuracies of 67.8% (hydration time), 83.3% (curing regime), and 87.6% (surface type), demonstrating the feasibility of using neural networks for hydration monitoring. In this study, the results revealed differences in near-infrared spectral signatures, representing the type of curing treatment, curing surface, and hydration time of the concrete. The dataset was classified and analyzed using neural networks. For each hydration treatment, three different models were developed to achieve better prediction performance for hyperspectral imaging analysis. This method demonstrated a high level of reliability in investigating curing surface treatments, curing treatments, and hydration time. A recommended method for using hyperspectral imaging to evaluate the cured quality of concrete will be developed in future research. Full article

(This article belongs to the Special Issue Advances in Structural Health Monitoring of the Built Environment)

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16 pages, 3606 KiB

Open AccessArticle

Comparative Study on Rail Damage Recognition Methods Based on Machine Vision

by Wanlin Gao, Riqin Geng and Hao Wu

Infrastructures 2025, 10(7), 171; https://doi.org/10.3390/infrastructures10070171 - 4 Jul 2025

Viewed by 300

Abstract

With the rapid expansion of railway networks and increasing operational complexity, intelligent rail damage detection has become crucial for ensuring safety and improving maintenance efficiency. Traditional physical inspection methods (e.g., ultrasonic testing, magnetic flux leakage) are limited in terms of efficiency and environmental [...] Read more.

With the rapid expansion of railway networks and increasing operational complexity, intelligent rail damage detection has become crucial for ensuring safety and improving maintenance efficiency. Traditional physical inspection methods (e.g., ultrasonic testing, magnetic flux leakage) are limited in terms of efficiency and environmental adaptability. This study proposes a machine vision-based approach leveraging deep learning to identify four primary types of rail damages: corrugations, spalls, cracks, and scratches. A self-developed acquisition device collected 298 field images from the Chongqing Metro system, which were expanded into 1556 samples through data augmentation techniques (including rotation, translation, shearing, and mirroring). This study systematically evaluated three object detection models—YOLOv8, SSD, and Faster R-CNN—in terms of detection accuracy (mAP), missed detection rate (mAR), and training efficiency. The results indicate that YOLOv8 outperformed the other models, achieving an mAP of 0.79, an mAR of 0.69, and a shortest training time of 0.28 h. To further enhance performance, this study integrated the Multi-Head Self-Attention (MHSA) module into YOLO, creating MHSA-YOLOv8. The optimized model achieved a significant improvement in mAP by 10% (to 0.89), increased mAR by 20%, and reduced training time by 50% (to 0.14 h). These findings demonstrate the effectiveness of MHSA-YOLO for accurate and efficient rail damage detection in complex environments, offering a robust solution for intelligent railway maintenance. Full article

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21 pages, 4515 KiB

Open AccessArticle

Deep Learning- and Multi-Point Analysis-Based Systematic Deformation Warning for Arch Dams

by Tao Zhou, Xiubo Niu, Ning Ma, Futing Sun and Shilin Gong

Infrastructures 2025, 10(7), 170; https://doi.org/10.3390/infrastructures10070170 - 3 Jul 2025

Viewed by 262

Abstract

Deformation is a direct manifestation of structural changes that occur during the operation of arch dams, and the development of reliable deformation early warning indicators allows for their timely study. Considering that an arch dam is a systematic overall structure, it is necessary [...] Read more.

Deformation is a direct manifestation of structural changes that occur during the operation of arch dams, and the development of reliable deformation early warning indicators allows for their timely study. Considering that an arch dam is a systematic overall structure, it is necessary to systematically analyze the formulation of deformation early warning indicators and general early warning methods for this dam type. To this end, this study innovatively proposes a systematic early warning method for arch dams based on deep learning and a multi-measurement point analysis strategy. Firstly, the causal model (HST) is utilized to extract the environmental factors as convolutional neural network (CNN) array samples, and the absolute deformation residual sequences of multiple points are obtained by HST-MultiCNN. Secondly, combining this with principal component analysis, a systematic deformation residual index with multiple points is established. Then, the kernel function is used to simulate the distribution of the abovementioned indicators, and is combined with the idea of small probability to formulate the overall warning indicator. Finally, the Re-CNN strategy is used to train the mapping relationship between the multi-objective residuals and the system indicators, and the mapping relationship outlined above is then used to obtain the system indicators corresponding to real-time prediction values, which in turn determine the overall deformation state of arch dams. Analysis shows that the RMSE of the deformation output of the proposed monitoring method uses a value between 0.2284 and 0.2942, with satisfactory accuracy, and the overall deformation warning accuracy reaches 100%, which is significantly better than the comparison method, and effectively solves the primary defect of the traditional single-point analysis—failure to reflect the overall deformation condition. Full article

(This article belongs to the Topic Disaster Risk Management and Resilience)

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29 pages, 5719 KiB

Open AccessArticle

Cross-Floor Vibration Wave Propagation in High-Rise Industrial Buildings Under TMD Control

by Ruoyang Zhou and Xiaoxiong Zha

Infrastructures 2025, 10(7), 169; https://doi.org/10.3390/infrastructures10070169 - 3 Jul 2025

Viewed by 262

Abstract

High-rise industrial buildings are particularly susceptible to vibration-induced comfort issues, which can negatively impact both the health and productivity of workers and office staff. Unlike most existing studies that focus on local structural components, this study proposes and validates a wave propagation analysis [...] Read more.

High-rise industrial buildings are particularly susceptible to vibration-induced comfort issues, which can negatively impact both the health and productivity of workers and office staff. Unlike most existing studies that focus on local structural components, this study proposes and validates a wave propagation analysis (WPA) method to predict peak accelerations of the floor caused by excitations located on different floors. The method is validated through on-site vibration tests conducted on a high-rise industrial building with shared factory and office space. A simplified regression-based propagation equation is further developed to facilitate practical design applications. The regression parameters are fitted using theoretical calculation results, enabling rapid prediction of peak acceleration responses on the same or different floors. To enhance vibration control, tuned mass dampers (TMDs) are installed on selected floors, and additional tests are conducted with the TMDs activated. An insertion loss-based correction is introduced into the WPA framework to account for the TMD’s frequency-dependent attenuation effects. The extended method supports both accurate prediction of vibration reduction and optimisation of TMD placement across multiple floors in high-rise industrial buildings. Full article

(This article belongs to the Special Issue Advances in Reinforced Concrete Infrastructure: Enhancing Structural Resilience and Promoting Sustainability)

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22 pages, 4455 KiB

Open AccessArticle

Assessing Engineering Behavior of Fly Ash-Based Geopolymer Concrete: Empirical Modeling

by Ahmad B. Malkawi

Infrastructures 2025, 10(7), 168; https://doi.org/10.3390/infrastructures10070168 - 1 Jul 2025

Viewed by 379

Abstract

The present work investigates the characteristics of fly ash-based geopolymer concrete (GPC) in terms of compressive, splitting, and flexural strength, elasticity modulus, and stress–strain relationship. Datasets including 726 observations were collected from the sorted literature, and regression models were proposed. These models were [...] Read more.

The present work investigates the characteristics of fly ash-based geopolymer concrete (GPC) in terms of compressive, splitting, and flexural strength, elasticity modulus, and stress–strain relationship. Datasets including 726 observations were collected from the sorted literature, and regression models were proposed. These models were then validated using experimental results obtained from 12 different mixtures prepared and tested in this research. Finally, the models were compared with the current models of several codes for ordinary Portland cement (OPC) concrete. The proposed models provided good accuracy with a determination coefficient greater than 60% for all models; such a value is considered large enough for big datasets. The behavior of GPC is not well-represented by OPC concrete standards, and GPC also displays a lower elastic modulus at similar strength. A constitutive model is proposed to describe GPC’s full stress–strain response, with the resulting equations providing relatively accurate predictions of its mechanical behavior. Compared to OPC concrete, GPC shows notably greater deformation, characterized by a wider range and higher average for both strain at peak stress (mean 0.00296) and ultimate strain (mean 0.01002). This work’s results may prompt further detailed research on GPC’s mechanical and, importantly, structural behavior. Full article

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

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14 pages, 1775 KiB

Open AccessArticle

Characterization of Recycled Aggregates from Building Demolition Waste for Use in Road Infrastructures

by Majid Ahmadpour, Davood Akbarimehr, Mohammad Rahai and Ali Momeni

Infrastructures 2025, 10(7), 167; https://doi.org/10.3390/infrastructures10070167 - 1 Jul 2025

Viewed by 255

Abstract

In light of rising environmental concerns, the rapid industrial recycling of building demolition waste material (BDWM) is now capable of supporting sustainable development in metropolitan regions. From this perspective, the current study investigated the geotechnical properties and applications of BDWMs as substitutes for [...] Read more.

In light of rising environmental concerns, the rapid industrial recycling of building demolition waste material (BDWM) is now capable of supporting sustainable development in metropolitan regions. From this perspective, the current study investigated the geotechnical properties and applications of BDWMs as substitutes for natural materials (NMs) in road engineering infrastructures. For this purpose, the physical and geotechnical characteristics of both types of materials were initially examined, and then compared using laboratory-scale material comprehensive assessments such as sieve analysis (SA), the flakiness index (FI), the specific gravity test (Gs), the Los Angeles abrasion test (LAAT), Atterberg limits (AL), the water absorption test (WAT), the California bearing ratio (CBR), the direct shear test (DST), and the Proctor soil compaction test (PSCT). The BDWMs were collected from two locations in Iran. According to the results, the collected samples consisted of concrete, bricks, mortar, tile materials, and others. The CBR values for the waste material from the two sites were 69 and 73%, respectively. Furthermore, the optimum water content (OWC) and maximum dry unit weight (MDD) from the two sites were reported as 9.3 and 9.9% and 20.8 and 21 kN/m³, respectively, and the hydrogen potential (pH) as 9 and 10. The shear strength and CBR values indicated that the BDWM had a suitable strength compared to the NM. In terms of road infrastructure applications, the shear strengths were adequate for the analysis of common sub-base materials used in filling and road construction. Furthermore, the study’s findings revealed that BDWMs were suitable replacements for the NM used in road engineering operations and could make a significant contribution to sustainable development. Full article

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24 pages, 5027 KiB

Open AccessArticle

Enhanced Prediction and Uncertainty Modeling of Pavement Roughness Using Machine Learning and Conformal Prediction

by Sadegh Ghavami, Hamed Naseri and Farzad Safi Jahanshahi

Infrastructures 2025, 10(7), 166; https://doi.org/10.3390/infrastructures10070166 - 30 Jun 2025

Viewed by 360

Abstract

Pavement performance models are considered a key element in pavement management systems since they can predict the future condition of pavements using historical data. Several indicators are used to evaluate the condition of pavements (such as the pavement condition index, rutting depth, and [...] Read more.

Pavement performance models are considered a key element in pavement management systems since they can predict the future condition of pavements using historical data. Several indicators are used to evaluate the condition of pavements (such as the pavement condition index, rutting depth, and cracking severity), and the international roughness index (IRI), which is the most widely employed worldwide. This study aimed to develop an accurate IRI prediction model. Ten prediction methods were trained on a dataset of 35 independent variables. The performance of the methods was compared, and the light gradient boosting machine was identified as the best-performing method for IRI prediction. Then, the SHAP was synchronized with the best-performing method to prioritize variables based on their relative influence on IRI. The results suggested that initial IRI, mean annual temperature, and the duration between data collections had the strongest relative influence on IRI prediction. Another objective of this study was to determine the optimal uncertainty model for IRI prediction. In this regard, 12 uncertainty models were developed based on different conformal prediction methods. Gray relational analysis was performed to identify the optimal uncertainty model. The results showed that Minmax/80 was the optimal uncertainty model for IRI prediction, with an effective coverage of 93.4% and an average interval width of 0.256 m/km. Finally, a further analysis was performed on the outcomes of the optimal uncertainty model, and initial IRI, duration, annual precipitation, and a few distress parameters were identified as uncertain. The results of the framework indicate in which situations the predicted IRI may be unreliable. Full article

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16 pages, 2204 KiB

Open AccessReview

Overview of the Patents and Patent Applications on Upper Guardrail Protection Systems for Motorcyclists

by Laura Brigita Parežnik, Marko Renčelj and Tomaž Tollazzi

Infrastructures 2025, 10(7), 165; https://doi.org/10.3390/infrastructures10070165 - 30 Jun 2025

Viewed by 291

Abstract

Upright-posture motorcycle crashes against steel safety barriers (SSBs) often result in severe upper-body injuries due to the sharp upper edge of the rail. While solutions for sliding crashes on curves, called a ‘motorcyclist-friendly barrier’, are already implemented in practice, protective measures for upright-posture [...] Read more.

Upright-posture motorcycle crashes against steel safety barriers (SSBs) often result in severe upper-body injuries due to the sharp upper edge of the rail. While solutions for sliding crashes on curves, called a ‘motorcyclist-friendly barrier’, are already implemented in practice, protective measures for upright-posture impacts remain underdeveloped. This study systematically reviews patents and patent applications addressing upper guardrail protection for motorcyclists. We identified and analysed a small number of existing innovations aimed at mitigating the consequences of upright crashes. The selected solutions were evaluated according to their technical design, ease of installation, potential for recycling, environmental compatibility, and expected costs. Our comparative analysis reveals that while some patents or patent applications offer promising features, such as flexible caps, bent plates, or modular attachments, none comprehensively address all safety, environmental, and economic requirements. The findings provide a basis for further development of motorcyclist-friendly SSB designs and suggest specific criteria that should be included in future guidelines and standard updates. Full article

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

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18 pages, 2320 KiB

Open AccessArticle

How Does Urban Rail Transit Density Affect Jobs–Housing Balance? A Case Study of Beijing

by Chang Ma and Kehu Tan

Infrastructures 2025, 10(7), 164; https://doi.org/10.3390/infrastructures10070164 - 30 Jun 2025

Viewed by 290

Abstract

Jobs–housing balance is a critical concern in urban planning and sustainable economic development. Urban rail transit, as a key determinant of employment and residential location decisions, plays a pivotal role in shaping jobs–housing dynamics. Beijing, the first Chinese city to develop a subway [...] Read more.

Jobs–housing balance is a critical concern in urban planning and sustainable economic development. Urban rail transit, as a key determinant of employment and residential location decisions, plays a pivotal role in shaping jobs–housing dynamics. Beijing, the first Chinese city to develop a subway system, offers a comprehensive rail network, making it an ideal case for exploring the effects of transit density on jobs–housing balance. This study utilizes medium-scale panel data from Beijing (2009–2022) and employs a fixed-effects model to systematically examine the impact of rail transit station density on jobs–housing balance and its underlying mechanisms. The results indicate that increasing transit station density tends to aggravate jobs–housing separation overall, with pronounced effects in central and outer suburban areas but negligible effects in near suburban areas. Mechanism analysis reveals two primary pathways: (1) improved accessibility draws employment toward transit-rich areas, reinforcing the attractiveness of central districts; (2) rising housing prices elevate residential thresholds, pushing lower-income populations toward outer suburbs. While enhanced transit density improves commuting convenience, it does not effectively reduce jobs–housing separation. These findings offer important policy implications for optimizing transit planning, improving jobs–housing alignment, and promoting sustainable urban development. Full article

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18 pages, 1057 KiB

Open AccessArticle

Crash Severity in Collisions with Roadside Light Poles: Highlighting the Potential of Passive Safe Pole Solutions

by Višnja Tkalčević Lakušić, Marija Ferko and Darko Babić

Infrastructures 2025, 10(7), 163; https://doi.org/10.3390/infrastructures10070163 - 30 Jun 2025

Viewed by 270

Abstract

This paper investigates crash severity in single-vehicle road crashes involving collisions with roadside light poles in Croatia. Due to the absence of detailed object-type classifications in the official crash database, media reports were used to identify relevant incidents in combination with the official [...] Read more.

This paper investigates crash severity in single-vehicle road crashes involving collisions with roadside light poles in Croatia. Due to the absence of detailed object-type classifications in the official crash database, media reports were used to identify relevant incidents in combination with the official state database, resulting in 38 crashes identified between 2016 and March 2025. Descriptive analysis and crosstabulation were applied to explore patterns in crash outcomes. A CHAID decision tree analysis was then applied in an exploratory capacity to highlight possible predictors of injury or fatal outcomes, acknowledging the limitations of the small sample size. Results showed that the speed limit was the only variable significantly associated with crash severity, with all crashes above 50 km/h resulting in injuries or fatalities. The findings highlight the importance of speed management and support the potential for implementing passively safe poles to reduce the consequences of such crashes. The study also discusses the performance of different pole types in line with EN 12767:2019, defines risk zones, and proposes solutions for the example locations. The results offer future research implications and valuable insights for road safety improvement, especially in areas with frequent pole collisions. Full article

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

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20 pages, 3047 KiB

Open AccessArticle

Comparison of Pulse-Echo Tomography and Through-Transmission Ultrasonic Test for UPV Characterization of Building Materials

by Emilia Vasanelli, Davide Di Gennaro, Matteo Sticchi, Gianni Blasi and Luigi Capozzoli

Infrastructures 2025, 10(7), 162; https://doi.org/10.3390/infrastructures10070162 - 27 Jun 2025

Viewed by 273

Abstract

Ultrasonic pulse velocity (UPV) is a widely used technique for diagnosis and structural safety assessment of existing buildings. The main difficulties in UPV tests on-site are due to one-sided accessibility of materials and degraded/irregular surfaces. Pulse-echo ultrasonic tomography (PE-UT) can overcome the problem. [...] Read more.

Ultrasonic pulse velocity (UPV) is a widely used technique for diagnosis and structural safety assessment of existing buildings. The main difficulties in UPV tests on-site are due to one-sided accessibility of materials and degraded/irregular surfaces. Pulse-echo ultrasonic tomography (PE-UT) can overcome the problem. Though it has been widely applied for detecting inhomogeneities within concrete, few works use the instrument to assess UPV. The present paper aims to fill the gap by comparing PE-UT results with those of through-transmission ultrasonic tests (TT-UT) commonly used for UPV characterization. TT-UT measurements were performed with cylindrical and exponential transducers. The latter are used on irregular surfaces or when coupling gel is forbidden. Few data are in the literature comparing exponential and cylindrical transducers’ results. This is a further element of novelty of the paper. PE-UT and TT-UT results were compared considering the effect of material compositeness, water, transmission mode, and transducer type. It was found that PE-UT allows for reliable and rapid one-sided measurements on concrete and stone in different conditions. The differences between PE-UT and TT-UT results were between 1 and 3%. Exponential transducers gave reliable results on fine-grained stone in direct transmission, with differences lower than 4% with cylindrical transducer results. Full article

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

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12 pages, 1982 KiB

Open AccessArticle

Concrete Bridge Crack Detection Using Unmanned Aerial Vehicles and Image Segmentation

by Yanli Chen, Hongze Li, Hang Zhu, Tianlong Ren and Zhe Cao

Infrastructures 2025, 10(7), 161; https://doi.org/10.3390/infrastructures10070161 - 27 Jun 2025

Viewed by 411

Abstract

Concrete bridge cracks are critical indicators for maintenance planning. Traditional visual inspections are often subjective, labor-intensive, and time-consuming, requiring close-range access by inspectors. In contrast, UAV-based remote sensing, combined with advanced image processing, offers a more efficient and accurate solution. This study proposes [...] Read more.

Concrete bridge cracks are critical indicators for maintenance planning. Traditional visual inspections are often subjective, labor-intensive, and time-consuming, requiring close-range access by inspectors. In contrast, UAV-based remote sensing, combined with advanced image processing, offers a more efficient and accurate solution. This study proposes an enhanced crack detection method combining Laplacian of Gaussian (LoG) filtering and Otsu thresholding to improve segmentation accuracy through background noise suppression. The proposed approach extracts key crack characteristics—including area, length, centroid, and main direction—enabling precise damage assessment. Experimental validation on a real bridge dataset demonstrates significant improvements in detection accuracy. The method provides a reliable tool for automated structural health monitoring, supporting data-driven maintenance decisions. Full article

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