Infrastructures

16 pages, 990 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

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)

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

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

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

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

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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29 pages, 4413 KiB

Open AccessArticle

Advancing Road Infrastructure Safety with the Remotely Piloted Safety Cone

by Francisco Javier García-Corbeira, David Alvarez-Moyano, Pedro Arias Sánchez and Joaquin Martinez-Sanchez

Infrastructures 2025, 10(7), 160; https://doi.org/10.3390/infrastructures10070160 - 27 Jun 2025

Abstract

This article presents the design, implementation, and validation of a Remotely Piloted Safety Cone (RPSC), an autonomous robotic system developed to enhance safety and operational efficiency in road maintenance. The RPSC addresses challenges associated with road works, including workers’ exposure to traffic hazards [...] Read more.

This article presents the design, implementation, and validation of a Remotely Piloted Safety Cone (RPSC), an autonomous robotic system developed to enhance safety and operational efficiency in road maintenance. The RPSC addresses challenges associated with road works, including workers’ exposure to traffic hazards and inefficiencies of traditional traffic cones, such as manual placement and retrieval, limited visibility in low-light conditions, and inability to adapt to dynamic changes in work zones. In contrast, the RPSC offers autonomous mobility, advanced visual signalling, and real-time communication capabilities, significantly improving safety and operational flexibility during maintenance tasks. The RPSC integrates sensor fusion, combining Global Navigation Satellite System (GNSS) with Real-Time Kinematic (RTK) for precise positioning, Inertial Measurement Unit (IMU) and encoders for accurate odometry, and obstacle detection sensors within an optimised navigation framework using Robot Operating System (ROS2) and Micro Air Vehicle Link (MAVLink) protocols. Complying with European regulations, the RPSC ensures structural integrity, visibility, stability, and regulatory compliance. Safety features include emergency stop capabilities, visual alarms, autonomous safety routines, and edge computing for rapid responsiveness. Field tests validated positioning accuracy below 30 cm, route deviations under 15 cm, and obstacle detection up to 4 m, significantly improved by Kalman filtering, aligning with digitalisation, sustainability, and occupational risk prevention objectives. Full article

(This article belongs to the Special Issue Maintaining Integrity, Performance and Safety of the Road Infrastructure through Autonomous Robotized Solutions and Modularization)

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29 pages, 2766 KiB

Open AccessArticle

Parameter Analysis of Pile Foundation Bearing Characteristics Based on Pore Water Pressure Using Rapid Load Test

by Jing-Jie Su, Xue-Liang Zhao, Qing Guo, Wei-Ming Gong, Yu-Chen Wang and Tong-Xing Zeng

Infrastructures 2025, 10(7), 159; https://doi.org/10.3390/infrastructures10070159 - 26 Jun 2025

Abstract

A novel approach for determining the bearing capacity of pile foundations using rapid load testing is suggested to rectify the inaccuracies arising from the presumption of a constant damping coefficient and excess pore water pressure during the evaluation of pile foundation bearing capacity [...] Read more.

A novel approach for determining the bearing capacity of pile foundations using rapid load testing is suggested to rectify the inaccuracies arising from the presumption of a constant damping coefficient and excess pore water pressure during the evaluation of pile foundation bearing capacity in soil. This research focuses on the characteristics associated with the segmented damping coefficient of pile foundations and the permeability coefficient of sand at the pile terminus, resulting in a long pulse vibration equation derived from dynamic effects. A numerical model incorporating the damping coefficient and permeability coefficient is developed, yielding the time history features of load, displacement, and acceleration. The findings indicate that (1) the long pulse vibration equation, predicated on dynamic effects, aligns more closely with the actual bearing capacity of pile foundations than traditional detection theory; (2) in the rapid load test method, the maximum load applied to sand pile foundations occurs prior to peak displacement, while the ultimate bearing capacity, after accounting for inertial forces, corresponds to the maximum displacement value; (3) the permeability coefficient significantly influences the ultra-static pore water pressure, and the testing error regarding the bearing capacity of low permeability sand pile foundations using the rapid loading method is elevated. Full article

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20 pages, 7094 KiB

Open AccessArticle

Adaptive Warning Thresholds for Dam Safety: A KDE-Based Approach

by Nathalia Silva-Cancino, Fernando Salazar, Joaquín Irazábal and Juan Mata

Infrastructures 2025, 10(7), 158; https://doi.org/10.3390/infrastructures10070158 - 26 Jun 2025

Abstract

Dams are critical infrastructures that provide essential services such as water supply, hydroelectric power generation, and flood control. As many dams age, the risk of structural failure increases, making safety assurance more urgent than ever. Traditional monitoring systems typically employ predictive models—based on [...] Read more.

Dams are critical infrastructures that provide essential services such as water supply, hydroelectric power generation, and flood control. As many dams age, the risk of structural failure increases, making safety assurance more urgent than ever. Traditional monitoring systems typically employ predictive models—based on techniques such as the finite element method (FEM) or machine learning (ML)—to compare real-time data against expected performance. However, these models often rely on static warning thresholds, which fail to reflect the dynamic conditions affecting dam behavior, including fluctuating water levels, temperature variations, and extreme weather events. This study introduces an adaptive warning threshold methodology for dam safety based on kernel density estimation (KDE). The approach incorporates a boosted regression tree (BRT) model for predictive analysis, identifying influential variables such as reservoir levels and ambient temperatures. KDE is then used to estimate the density of historical data, allowing for dynamic calibration of warning thresholds. In regions of low data density—where prediction uncertainty is higher—the thresholds are widened to reduce false alarms, while in high-density regions, stricter thresholds are maintained to preserve sensitivity. The methodology was validated using data from an arch dam, demonstrating improved anomaly detection capabilities. It successfully reduced false positives in data-sparse conditions while maintaining high sensitivity to true anomalies in denser data regions. These results confirm that the proposed methodology successfully meets the goals of enhancing reliability and adaptability in dam safety monitoring. This adaptive framework offers a robust enhancement to dam safety monitoring systems, enabling more reliable detection of structural issues under variable operating conditions. Full article

(This article belongs to the Special Issue Preserving Life Through Dams)

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19 pages, 3345 KiB

Open AccessArticle

AI for Predicting Pavement Roughness in Road Monitoring and Maintenance

by Christina Plati, Angeliki Armeni, Charis Kyriakou and Dimitra Asoniti

Infrastructures 2025, 10(7), 157; https://doi.org/10.3390/infrastructures10070157 - 26 Jun 2025

Abstract

In recent decades, numerous studies have investigated the application of Artificial Intelligence (AI), and more precisely of Artificial Neural Networks (ANNs), in the prediction of complex technical parameters, particularly in the field of road infrastructure management. Among them, prediction of the widely used [...] Read more.

In recent decades, numerous studies have investigated the application of Artificial Intelligence (AI), and more precisely of Artificial Neural Networks (ANNs), in the prediction of complex technical parameters, particularly in the field of road infrastructure management. Among them, prediction of the widely used International Roughness Index (IRI) has attracted much attention due to its importance in pavement maintenance planning. This study focuses on predicting future IRI values using traditional regression models and neural networks, specifically Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) networks, on two highway sections, each analyzed in two experimental setups. The models consider only traffic and structural road characteristics as variables. The results show that the LSTM method provides significantly lower prediction errors for both highway sections, indicating better performance in capturing roughness trends over time. These results confirm that ANNs are a useful tool for engineers by predicting future IRI values, as they help to extend pavement life and reduce overall maintenance costs. The integration of machine learning into pavement evaluation is a promising step forward in ongoing efforts to optimize pavement management. Full article

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

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20 pages, 635 KiB

Open AccessReview

Statistical Reliability Analysis for Assessing Bridge Structural Integrity: A Review Paper

by Gustavo S. P. Primo, Ramon Silva, Francisco Evangelista, Jr. and Marcos H. Oliveira

Infrastructures 2025, 10(7), 156; https://doi.org/10.3390/infrastructures10070156 - 25 Jun 2025

Abstract

This article reviews methods for estimating the remaining service life of bridges, focusing on the statistical analysis of reliability indices, which aids in identifying risks and predicting structural failures. Among the methodologies examined, the First-Order Reliability Method (FORM) is highlighted for its effectiveness [...] Read more.

This article reviews methods for estimating the remaining service life of bridges, focusing on the statistical analysis of reliability indices, which aids in identifying risks and predicting structural failures. Among the methodologies examined, the First-Order Reliability Method (FORM) is highlighted for its effectiveness in calculating failure probabilities based on current deterioration and loading conditions. Sensitivity analysis is also discussed, as it pinpoints the variables that most significantly impact structural stability. Enhanced using the Finite Element Method (FEM), this method allows the simulation of structural behavior across different deterioration scenarios, improving the precision of failure predictions and optimizing maintenance planning. This review provides insight into how the integration of probabilistic methods and sensitivity analysis can enhance failure prediction and support more efficient maintenance planning for bridge structures. Full article

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23 pages, 2630 KiB

Open AccessArticle

Machine Learning Traffic Flow Prediction Models for Smart and Sustainable Traffic Management

by Rusul Abduljabbar, Hussein Dia and Sohani Liyanage

Infrastructures 2025, 10(7), 155; https://doi.org/10.3390/infrastructures10070155 - 24 Jun 2025

Abstract

Sustainable traffic management relies on accurate traffic flow prediction to reduce congestion, fuel consumption, and emissions and minimise the external environmental impacts of traffic operations. This study contributes to this objective by developing and evaluating advanced machine learning models that leverage multisource data [...] Read more.

Sustainable traffic management relies on accurate traffic flow prediction to reduce congestion, fuel consumption, and emissions and minimise the external environmental impacts of traffic operations. This study contributes to this objective by developing and evaluating advanced machine learning models that leverage multisource data to predict traffic patterns more effectively, allowing for the deployment of proactive measures to prevent or reduce traffic congestion and idling times, leading to enhanced eco-friendly mobility. Specifically, this paper evaluates the impact of multisource sensor inputs and spatial detector interactions on machine learning-based traffic flow prediction. Using a dataset of 839,377 observations from 14 detector stations along Melbourne’s Eastern Freeway, Bidirectional Long Short-Term Memory (BiLSTM) models were developed to assess predictive accuracy under different input configurations. The results demonstrated that incorporating speed and occupancy inputs alongside traffic flow improves prediction accuracy by up to 16% across all detector stations. This study also investigated the role of spatial flow input interactions from upstream and downstream detectors in enhancing prediction performance. The findings confirm that including neighbouring detectors improves prediction accuracy, increasing performance from 96% to 98% for eastbound and westbound directions. These findings highlight the benefits of optimised sensor deployment, data integration, and advanced machine-learning techniques for smart and eco-friendly traffic systems. Additionally, this study provides a foundation for data-driven, adaptive traffic management strategies that contribute to sustainable road network planning, reducing vehicle idling, fuel consumption, and emissions while enhancing urban mobility and supporting sustainability goals. Furthermore, the proposed framework aligns with key United Nations Sustainable Development Goals (SDGs), particularly those promoting sustainable cities, resilient infrastructure, and climate-responsive planning. Full article

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

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18 pages, 1961 KiB

Open AccessArticle

Comparative Study of Machine Learning Techniques for Predicting UCS Values Using Basic Soil Index Parameters in Pavement Construction

by Mudhaffer Alqudah, Haitham Saleh, Hakan Yasarer, Ahmed Al-Ostaz and Yacoub Najjar

Infrastructures 2025, 10(7), 153; https://doi.org/10.3390/infrastructures10070153 - 24 Jun 2025

Abstract

This study investigated the prediction of unconfined compressive strength (UCS), a common measure of soil’s undrained shear strength, using fundamental soil characteristics. While traditional pavement subgrade design often relies on parameters like the resilient modulus and California bearing ratio (CBR), researchers are exploring [...] Read more.

This study investigated the prediction of unconfined compressive strength (UCS), a common measure of soil’s undrained shear strength, using fundamental soil characteristics. While traditional pavement subgrade design often relies on parameters like the resilient modulus and California bearing ratio (CBR), researchers are exploring the potential of incorporating more easily obtainable strength indicators, such as UCS. To evaluate the potential effectiveness of UCS for pavement engineering applications, a dataset of 152 laboratory-tested soil samples was compiled to develop predictive models. For each sample, geotechnical properties including the Atterberg limits, liquid limit (LL), plastic limit (PL), water content (WC), and bulk density (determined using the Harvard miniature compaction apparatus), alongside the UCS, were measured. This dataset served to train various models to estimate the UCS from basic soil parameters. The methods employed included multi-linear regression (MLR), multi-nonlinear regression (MNLR), and several machine learning techniques: backpropagation artificial neural networks (ANNs), gradient boosting (GB), random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN). The aim was to establish a relationship between the dependent variable (UCS) and the independent basic geotechnical properties and to test the effectiveness of each ML algorithm in predicting UCS. The results indicate that the ANN-based model provided the most accurate predictions for UCS, achieving an R² of 0.83, a root-mean-squared error (RMSE) of 1.11, and a mean absolute relative error (MARE) of 0.42. The performance ranking of the other models, from best to worst, was RF, GB, SV, KNN, MLR, and MNLR. Full article

(This article belongs to the Special Issue Advances in Materials and Technology for Sustainable and Smart Pavements)

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19 pages, 6160 KiB

Open AccessArticle

Prediction of Rail Wear Under Different Railway Track Geometries Using Artificial Neural Networks

by Hong Zhang, Weichen Shuai, Linya Liu, Pengfei Zhang, Kejun Zhang, Hongsong Lin, Yuke Zhang and Wei Li

Infrastructures 2025, 10(7), 154; https://doi.org/10.3390/infrastructures10070154 - 23 Jun 2025

Abstract

The geometry of the railway track affects rail wear significantly. If the rail wear can be predicted and considered during the alignment design phase, the problems it causes can be mitigated at the source by optimizing the values and combinations of railway track [...] Read more.

The geometry of the railway track affects rail wear significantly. If the rail wear can be predicted and considered during the alignment design phase, the problems it causes can be mitigated at the source by optimizing the values and combinations of railway track geometry parameters. However, the relationship between railway track geometry and rail wear remains unclear. It is hard to acquire rail wear data for different alignments with varying geometric parameters during the alignment design phase. This study develops a PSO-ANN model to establish the mapping relationship between railway track geometry and rail wear, enabling prediction of rail wear based on track geometry parameters. The model achieves prediction accuracies of 96.70% for inner rail wear and 98.13% for outer rail wear. Compared with the conventional ANN model, the PSO-ANN model reduces the prediction errors by 22.54% for inner rail wear and 55.69% for outer rail wear. Sobol sensitivity analysis is conducted to analyze the influence of the track geometry parameters on rail wear, revealing that inner rail wear is mainly affected by curve radius, transition curve length, and superelevation, while outer rail wear is predominantly influenced by curve radius. Full article

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23 pages, 5438 KiB

Open AccessArticle

Exposure Modeling of Transmission Towers for Large-Scale Natural Hazard Risk Assessments Based on Deep-Learning Object Detection Models

by Luigi Cesarini, Rui Figueiredo, Xavier Romão and Mario Martina

Infrastructures 2025, 10(7), 152; https://doi.org/10.3390/infrastructures10070152 - 23 Jun 2025

Abstract

Exposure modeling plays a crucial role in disaster risk assessments by providing geospatial information about assets at risk and their characteristics. Detailed exposure data enhances the spatial representation of a rapidly changing environment, enabling decision-makers to develop effective policies for reducing disaster risk. [...] Read more.

Exposure modeling plays a crucial role in disaster risk assessments by providing geospatial information about assets at risk and their characteristics. Detailed exposure data enhances the spatial representation of a rapidly changing environment, enabling decision-makers to develop effective policies for reducing disaster risk. This work proposes and demonstrates a methodology linking volunteered geographic information from OpenStreetMap (OSM), street-level imagery from Google Street View (GSV), and deep learning object detection models into the automated creation of exposure datasets for power grid transmission towers, assets particularly vulnerable to strong wind, and other perils. Specifically, the methodology is implemented through a start-to-end pipeline that starts from the locations of transmission towers derived from OSM data to obtain GSV images capturing the towers in a given region, based on which their relevant features for risk assessment purposes are determined using two families of object detection models, i.e., single-stage and two-stage detectors. Both models adopted herein, You Only Look Once version 5 (YOLOv5) and Detectron2, achieved high values of mean average precision (mAP) for the identification task (83.67% and 88.64%, respectively), while Detectron2 was found to outperform YOLOv5 for the classification task with a mAP of 64.89% against a 50.62% of the single-stage detector. When applied to a pilot study area in northern Portugal comprising approximately 5.800 towers, the two-stage detector also exhibited higher confidence in its detection on a larger part of the study area, highlighting the potential of the approach for large-scale exposure modeling of transmission towers. Full article

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

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25 pages, 4031 KiB

Open AccessArticle

Highway Rest Area Truck Parking Occupancy Prediction Using Machine Learning: A Case Study from Poland

by Artur Budzyński and Maria Cieśla

Infrastructures 2025, 10(7), 151; https://doi.org/10.3390/infrastructures10070151 - 22 Jun 2025

Abstract

Highway rest areas are relevant components of road infrastructure, providing drivers with essential opportunities to rest and mitigate fatigue-related crash risks. Despite their acknowledged importance, little is known about the factors that influence their actual utilization. This study addresses this gap by applying [...] Read more.

Highway rest areas are relevant components of road infrastructure, providing drivers with essential opportunities to rest and mitigate fatigue-related crash risks. Despite their acknowledged importance, little is known about the factors that influence their actual utilization. This study addresses this gap by applying supervised machine learning algorithms to predict hourly occupancy levels of truck parking lots at highway rest areas using a dataset collected from digital monitoring systems in Poland. The dataset includes 10,740 observations and 33 features describing infrastructural, administrative, and locational characteristics of selected rest areas in Poland. Eight classification models—Gradient Boosting, XGBoost, Random Forest, k-NN, Decision Tree, Logistic Regression, SVM, and Naive Bayes—were implemented and compared using standard performance metrics. Gradient Boosting emerged as the best-performing model, achieving the highest prediction accuracy and identifying key features such as the presence of fuel stations, rest area category, and facility amenities as significant predictors of occupancy. The findings highlight the potential of interpretable machine learning methods for supporting infrastructure planning, particularly in identifying underutilized or overburdened facilities. This research demonstrates a data-driven approach for analyzing rest area usage and provides practical insights for optimizing facility distribution, enhancing road safety, and informing future investments in transport infrastructure. Full article

(This article belongs to the Special Issue Smart Mobility and Transportation Infrastructure)

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19 pages, 1137 KiB

Open AccessArticle

Quantitative Evaluation of Sustainable Construction and Demolition Waste Management System Performance in South Africa

by Ademilade Olubambi, Opeoluwa Akinradewo, Clinton Aigbavboa and Bolanle Ikotun

Infrastructures 2025, 10(6), 150; https://doi.org/10.3390/infrastructures10060150 - 18 Jun 2025

Abstract

In South Africa, inefficient resource utilization in waste management results in a preference for disposal and landfilling as the lowest tier within the waste management hierarchy. Through a methodical approach to waste management system performance evaluation, using sustainability indicators, this study assists the [...] Read more.

In South Africa, inefficient resource utilization in waste management results in a preference for disposal and landfilling as the lowest tier within the waste management hierarchy. Through a methodical approach to waste management system performance evaluation, using sustainability indicators, this study assists the construction industry to precisely define the current state of its waste management practice. This study conducted a comprehensive literature analysis to choose metrics that meet sustainability standards. To illustrate sustainability considerations across all lifetime dimensions, a table with twenty-two indicators was created. To enable sustainable measurement utilizing the triple-line dimension, a model-material flow system with a life-cycle mapping was modified. Exploratory factor analysis (EFA) was used to extract data. At each phase of the building lifespan, the sustainability performance measurement was carried out and validated. The findings indicate that sustainability was quantified at 0.5150 during the planning and design phase, with 0.4125 interpreted as below-average performance score during the initiation and feasibility testing phase, and with 0.500 during procurement, 0.5137 during construction and execution phases, 0.5250 during performance monitoring, 0.5350 during post-construction, and 0.5050 during renovation all having an average performance score. The waste management systems’ overall cumulative sustainability performance was determined to be 0.5009. The overall performance of the current waste management systems is satisfactory, but require improvement. Therefore, the government can use this sustainability appraisal to adopt a top-level policy for a sustainable waste industry in South Africa as part of its growing pursuit of sustainable development. Full article

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21 pages, 7032 KiB

Open AccessArticle

Influence of Moisture on the Shakedown Behavior of Fine Soils for Sustainable Railway Subballast Layers

by William Wilson dos Santos, Gleyciane Almeida Serra, Lisley Madeira Coelho, Sergio Neves Monteiro, Gabriel de Carvalho Nascimento and Antônio Carlos Rodrigues Guimarães

Infrastructures 2025, 10(6), 149; https://doi.org/10.3390/infrastructures10060149 - 18 Jun 2025

Abstract

This study investigates the influence of moisture on the mechanical behavior of fine soil mixtures from the São Luís region, applied as subballast layers in railway track structures. Two samples were analyzed: a non-lateritic sandy soil (NA’, AM03) and a lateritic clayey soil [...] Read more.

This study investigates the influence of moisture on the mechanical behavior of fine soil mixtures from the São Luís region, applied as subballast layers in railway track structures. Two samples were analyzed: a non-lateritic sandy soil (NA’, AM03) and a lateritic clayey soil (LG’, AM09). The research included physical and chemical characterization tests, as well as repeated load triaxial tests to determine the resilient modulus and shakedown limits, complemented by numerical simulations using the SysTrain 2.0 software. The samples showed average resilient modulus values of 577 MPa and 638 MPa, respectively. Tests were conducted under optimum moisture content and under moisture 1% above the optimum, induced by capillary rise in compacted samples. The results indicated that under 1% above optimum moisture, the shakedown limits were reduced by up to 50% for AM03 and 25% for AM09, demonstrating greater stability for the lateritic soil. In addition, it was observed that as stress ratios increased, the shakedown limits for both moisture conditions tended to converge. Numerical simulations confirmed the adverse influence of increased moisture on the occurrence of shakedown in both samples. For AM03, the simulations revealed progressive failure under elevated moisture, indicating a more severe stress redistribution within the subballast layer. In contrast, AM09 remained within the shakedown regime under both conditions, although it exhibited higher values of

S_{1} / S_{1 \max}

under moisture above optimum, suggesting a greater tendency toward plastic creep. These findings highlight the critical importance of moisture control for the sustainable performance of railway substructures. This study contributes to understanding environmental vulnerability in transportation infrastructure and supports the development of more resilient and sustainable railway systems. Full article

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32 pages, 39053 KiB

Open AccessReview

Review of Brillouin Distributed Sensing for Structural Monitoring in Transportation Infrastructure

by Bin Lv, Yuqing Peng, Cong Du, Yuan Tian and Jianqing Wu

Infrastructures 2025, 10(6), 148; https://doi.org/10.3390/infrastructures10060148 - 16 Jun 2025

Abstract

Distributed optical fiber sensing (DOFS) is an advanced tool for structural health monitoring (SHM), offering high precision, wide measurement range, and real-time as well as long-term monitoring capabilities. It enables real-time monitoring of both temperature and strain information along the entire optical fiber [...] Read more.

Distributed optical fiber sensing (DOFS) is an advanced tool for structural health monitoring (SHM), offering high precision, wide measurement range, and real-time as well as long-term monitoring capabilities. It enables real-time monitoring of both temperature and strain information along the entire optical fiber line, providing a novel approach for safety monitoring and structural health assessment in transportation engineering. This paper first introduces the fundamental principles and classifications of DOFS technology and then systematically reviews the current research progress on Brillouin scattering-based DOFS. By analyzing the monitoring requirements of various types of transportation infrastructure, this paper discusses the applications and challenges of this technology in SHM and damage detection for roads, bridges, tunnels, and other infrastructure, particularly in identifying and tracking cracks, deformations, and localized damage. This review highlights the significant potential and promising prospects of Brillouin scattering technology in transportation engineering. Nevertheless, further research is needed to optimize sensing system performance and promote its widespread application in this field. These findings provide valuable references for future research and technological development. Full article

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27 pages, 1470 KiB

Open AccessReview

Beyond Speed Reduction: A Systematic Literature Review of Traffic-Calming Effects on Public Health, Travel Behaviour, and Urban Liveability

by Fotios Magkafas, Grigorios Fountas, Panagiotis Ch. Anastasopoulos and Socrates Basbas

Infrastructures 2025, 10(6), 147; https://doi.org/10.3390/infrastructures10060147 - 16 Jun 2025

Abstract

Traffic calming has emerged as a key urban strategy to reduce vehicle speeds and mitigate road traffic risks, with increasing recognition of its broader implications for public health, human behaviour, and urban liveability. This systematic literature review examines the multifaceted impacts of traffic-calming [...] Read more.

Traffic calming has emerged as a key urban strategy to reduce vehicle speeds and mitigate road traffic risks, with increasing recognition of its broader implications for public health, human behaviour, and urban liveability. This systematic literature review examines the multifaceted impacts of traffic-calming measures—from speed limit reductions to physical infrastructure and enforcement-based interventions—by synthesising findings from 28 peer-reviewed studies. Guided by the PRISMA framework, the review compiles research exploring links between traffic calming and outcomes related to public health, behaviour, and urban quality of life. Research consistently indicates that such interventions reduce both the frequency and severity of collisions, improve air and noise quality, and promote active mobility. These effects are shaped by user perceptions: non-motorised users tend to report higher levels of safety and accessibility, whereas motorised users often express frustration or resistance. Beyond safety and environmental improvements, traffic calming has been associated with greater use of public space, stronger social connections, and enhanced environmental aesthetics. The findings also show that key challenges may affect the effectiveness of traffic calming and these include negative attitudes among drivers, mixed outcomes for air quality, and unintended consequences such as traffic displacement or increased noise when interventions are poorly implemented. Overall, the findings suggest that traffic calming can serve as both a public health initiative and a tool for enhancing urban liveability, provided that the measures are designed with contextual sensitivity and supported by inclusive communication strategies. Full article

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

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