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Infrastructures
Volume 10
Issue 5
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Infrastructures, Volume 10, Issue 5 (May 2025) – 25 articles

Cover Story (view full-size image): Urban flooding presents challenges to coastal lowland cities, particularly with the impacts of climate change. This study introduces a multifunctional framework to assess polder design performance for flood protection, integrating hydraulic effectiveness with socioenvironmental aspects and emphasizing blue–green infrastructure as a key element. The following three design scenarios were modeled for Jardim Maravilha, Rio de Janeiro: S1 (urban-adjacent dike, pump-based) and S2/S3 (riverbank-aligned dike, gravity-driven). S2/S3 offered greater storage, freeboard resilience, and urban integration via floodable parks, while S1 drained faster but had reduced freeboard under climate stress. A new standardized scoring system revealed trade-offs, with S3 emerging as the most balanced option. The approach supports adaptive, sustainable flood management in vulnerable coastal cities. View this paper
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27 pages, 11744 KiB  
Article
Enhancing Railway Track Intervention Planning: Accounting for Component Interactions and Evolving Failure Risks
by Hamed Mehranfar, Bryan T. Adey, Saviz Moghtadernejad and Claudia Fecarotti
Infrastructures 2025, 10(5), 126; https://doi.org/10.3390/infrastructures10050126 - 21 May 2025
Viewed by 411
Abstract
This manuscript proposes a methodology to leverage digitalisation to efficiently generate an overview of required condition-based railway track interventions, possession windows, and expected costs for railway networks at the beginning of the intervention planning process. The consistent and efficient generation of such an [...] Read more.
This manuscript proposes a methodology to leverage digitalisation to efficiently generate an overview of required condition-based railway track interventions, possession windows, and expected costs for railway networks at the beginning of the intervention planning process. The consistent and efficient generation of such an overview not only helps track managers in their decision-making but also facilitates the discussion among other decision-makers in later phases of the track intervention planning process, including line planners, capacity managers, and project managers. The methodology uses data of different levels of detail, discrete state modelling for uncertain deterioration of components, and component-level intervention strategies. It dynamically updates the condition estimates of components by capturing the interaction between deteriorating components using Bayesian filters. It also estimates the risks associated with different types of potential service losses that may occur due to sudden events using fault trees as a function of time and the condition of components. An implementation of the methodology is conducted for a 25 km regional railway network in Switzerland. The results suggest that the methodology has the potential to help track managers early in the intervention planning process. In addition, it is argued that the methodology will lead to improvements in the efficiency of the planning process, improvements in the scheduling of preventive interventions, and the reduction in corrective intervention costs upon the implementation in a digital environment. Full article
(This article belongs to the Section Infrastructures Inspection and Maintenance)
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13 pages, 5874 KiB  
Article
An Investigation on Prediction of Infrastructure Asset Defect with CNN and ViT Algorithms
by Nam Lethanh, Tu Anh Trinh and Mir Tahmid Hossain
Infrastructures 2025, 10(5), 125; https://doi.org/10.3390/infrastructures10050125 - 20 May 2025
Viewed by 580
Abstract
Convolutional Neural Networks (CNNs) have been demonstrated to be one of the most powerful methods for image recognition, being applied in many fields, including civil and structural health monitoring in infrastructure asset management. Current State-of-the-Art CNN models are now accessible as open-source and [...] Read more.
Convolutional Neural Networks (CNNs) have been demonstrated to be one of the most powerful methods for image recognition, being applied in many fields, including civil and structural health monitoring in infrastructure asset management. Current State-of-the-Art CNN models are now accessible as open-source and available on several Artificial Intelligence (AI) platforms, with TensorFlow being widely used. Besides CNN models, Vision Transformers (ViTs) have recently emerged as a competitive alternative. Several demonstrations have indicated that ViT models, in many instances, outperform the current CNNs by almost four times in terms of computational efficiency and accuracy. This paper presents an investigation into defect detection for civil and structural components using CNN and ViT models available on TensorFlow. An empirical study was conducted using a database of cracks. The severity of crack is categorized into binary states: “with crack” and “without crack”. The results confirm that the accuracies of both CNN and ViT models exceed 95% after 100 epochs of training, with no significant difference observed between them for binary classification. Notably, the cost of this AI-based approach with images taken by lightweight and low-cost drones is considerably lower compared to high-speed inspection cars, while still delivering an expected level of predictive accuracy. Full article
(This article belongs to the Section Infrastructures Inspection and Maintenance)
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26 pages, 7349 KiB  
Article
Performance of High Strength Fiber Reinforced Mortar Made with Ceramic Powder, Metakaolin, and Magnetized Water
by Osama Youssf, Khalid A. Eltawil, Mohamed M. Yousry Elshikh and Mostafa M. Keshta
Infrastructures 2025, 10(5), 124; https://doi.org/10.3390/infrastructures10050124 - 19 May 2025
Cited by 1 | Viewed by 494
Abstract
In recent years, there has been a notable concern about the production of cementitious composites due to its high cement consumption and the corresponding carbon footprint. This has led to significant progress within the construction sector in integrating various waste materials as cement [...] Read more.
In recent years, there has been a notable concern about the production of cementitious composites due to its high cement consumption and the corresponding carbon footprint. This has led to significant progress within the construction sector in integrating various waste materials as cement alternatives into cementitious composites. In this study, a sustainable high strength fiber reinforced mortar (HS-FRM) was designed with ceramic powder (CP) and metakaolin (MK) materials as partial replacements of the conventional HS-FRM by up to 80%. Magnetized water (MW) was used in the proposed HS-FRM as mixing water and replaced the normal tap water (TW) for producing a more sustainable and higher strength cementitious product. The HS-FRM was cured using four different curing methods, namely, tap water, seawater, air, and sunlight. Fresh, mechanical, durability, and microstructure characteristics were measured and analyzed for the proposed HS-FRM. The results showed that CP can enhance the slump of HS-FRM by up to 50% (achieved at 40% CP), while MK showed the same or less slump (by up to 33%) than that of the conventional HS-FRM. Using up to 80% of either CP or MK in the HS-FRM continuously decreased its 28-day compressive strength by up to 78% or 83%, respectively. The HS-FRM cured in tap water exhibited the highest compressive strength compared to the other curing conditions. The use of MW improved the workability of the HS-FRM by up to 225% and the compressive strength by up to 13%. The microstructure analyses interpreted the reported variation in the HS-FRM compressive strength and showed that using MW in the HS-FRM revealed a dense structure with an adequate bond between the fiber and the matrix with a relatively low number of micro-cracks and pores compared when using TW. The XRD analysis showed higher peaks of Q, C, and L with the presence of MW compared to mixtures made with TW. Full article
(This article belongs to the Special Issue Advances in Reinforced Concrete Infrastructure: Enhancing Structural Resilience and Promoting Sustainability)
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17 pages, 685 KiB  
Article
Fragility-Based Seismic Risk Assessment of Reinforced Concrete Bridge Columns
by Mohamad Nassar and Ahmad Abo El Ezz
Infrastructures 2025, 10(5), 123; https://doi.org/10.3390/infrastructures10050123 - 16 May 2025
Viewed by 502
Abstract
In earthquake-prone regions, predicting the impact of seismic events on highway bridges is crucial for post-earthquake effective emergency response and recovery planning. This paper presents a methodology for a simplified seismic risk assessment of bridges using fragility curves that integrates updated ductility ratios [...] Read more.
In earthquake-prone regions, predicting the impact of seismic events on highway bridges is crucial for post-earthquake effective emergency response and recovery planning. This paper presents a methodology for a simplified seismic risk assessment of bridges using fragility curves that integrates updated ductility ratios of reinforced concrete bridge columns from literature based on experimental results on cyclic tests of reinforced concrete circular columns. The methodology considers two damage states (cover spalling and bar buckling) for bridge columns with seismic and non-seismic design considerations and then estimates displacement thresholds for each damage state. The Damage Margin Ratio (DMR) is introduced as an index defined by the ratio of the median Peak Ground Acceleration (PGA) for a specific damage state to the PGA that corresponds to the target seismic hazard probability of exceedance in 50 years that is typically defined in bridge design and evaluation codes and standards. The DMR is then compared to a user-specified Threshold Damage Margin Ratio (TDMR) to evaluate the level of risk at a specific threshold probability of exceedance of the damage state (5% and 10%). Comparative assessment is conducted for the relative seismic risk and performance of non-seismic and seismic bridges corresponding to the seismic hazard values at 10% and 2% probability of exceedance in 50 years for 7 urban centers in the province of Quebec as a case study demonstration of the methodology. The proposed methodology offers a rapid tool for screening and prioritizing bridges for detailed seismic evaluation. Full article
(This article belongs to the Section Infrastructures and Structural Engineering)
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24 pages, 2477 KiB  
Article
Analysis and Prediction of Traffic Conditions Using Machine Learning Models on Ikorodu Road in Lagos State, Nigeria
by Udeme Udo Imoh and Majid Movahedi Rad
Infrastructures 2025, 10(5), 122; https://doi.org/10.3390/infrastructures10050122 - 16 May 2025
Viewed by 1915
Abstract
Traffic counts are essential for assessing road capacity to provide efficient, effective, and safe mobility. However, current methods for generating models for traffic count studies are often limited in their accuracy and applicability, which can lead to incorrect or imprecise estimates of traffic [...] Read more.
Traffic counts are essential for assessing road capacity to provide efficient, effective, and safe mobility. However, current methods for generating models for traffic count studies are often limited in their accuracy and applicability, which can lead to incorrect or imprecise estimates of traffic volume. This study focused on analyzing and predicting traffic conditions on Ikorodu Road in Lagos State. The analysis involved an examination of historical traffic data, specifically focusing on daily and hourly traffic volumes. The prediction involved the use of machine learning models, including decision trees, gradient boosting, and random forest classifiers. The results of this study revealed significant variations in traffic volume across different days of the week and times of the day, indicating peak and off-peak periods. The study also highlighted the need for a more comprehensive approach that includes additional factors, such as weather conditions, road work, and special events, which could significantly impact traffic volume. Full article
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16 pages, 7146 KiB  
Article
Numerical Simulation and Analysis of the Influencing Factors of Ice Formation on Electrified Railway Contact Lines
by Changyi Liu, Yifan Zhang, Wei Ma and Yang Song
Infrastructures 2025, 10(5), 121; https://doi.org/10.3390/infrastructures10050121 - 15 May 2025
Viewed by 505
Abstract
This study focuses on the icing problem of electrified railway contact lines. Using computational fluid dynamic (CFD) numerical simulations, a three-dimensional mesh model of the CuAg0.1AC120 contact line was developed. This study reveals the effects of environmental factors such as droplet diameter, air–liquid [...] Read more.
This study focuses on the icing problem of electrified railway contact lines. Using computational fluid dynamic (CFD) numerical simulations, a three-dimensional mesh model of the CuAg0.1AC120 contact line was developed. This study reveals the effects of environmental factors such as droplet diameter, air–liquid water content (LWC), and ambient temperature on the icing morphology. The results show that the asymmetric cross-sectional structure of the contact line causes localized droplet accumulation in the groove areas, leading to distinctly non-uniform and directional ice formation. At high wind speeds, secondary icing is observed on the leeward side, where droplets are carried by bypass airflow—this phenomenon is not prominent in standard conductors. Additionally, the contact line exhibits a more sensitive response to temperature and air moisture content changes, suggesting that it is more suited to a localized anti-icing strategy. The numerical model developed in this study provides a theoretical foundation for predicting ice loads on complex section conductors and supports the design optimization and maintenance of high-speed railway catenary systems. Full article
(This article belongs to the Special Issue The Resilience of Railway Networks: Enhancing Safety and Robustness)
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21 pages, 1427 KiB  
Article
Cellular Automata for Optimization of Traffic Emission and Flow Dynamics in Two-Route Systems Using Feedback Information
by Rachid Marzoug, Noureddine Lakouari, José Roberto Pérez Cruz, Beatriz Castillo-Téllez, Gerardo Alberto Mejía-Pérez and Omar Bamaarouf
Infrastructures 2025, 10(5), 120; https://doi.org/10.3390/infrastructures10050120 - 14 May 2025
Viewed by 482
Abstract
Managing emissions and congestion in urban transportation systems is a growing challenge, particularly when traffic dynamics are influenced by real-time conditions and infrastructure constraints. This study addresses this issue by proposing a cellular automata-based model to analyze traffic emissions and flow dynamics in [...] Read more.
Managing emissions and congestion in urban transportation systems is a growing challenge, particularly when traffic dynamics are influenced by real-time conditions and infrastructure constraints. This study addresses this issue by proposing a cellular automata-based model to analyze traffic emissions and flow dynamics in two-route traffic systems under one-directional flow conditions, incorporating various real-time information feedback strategies. Unlike previous studies, the proposed model integrates key components of urban infrastructure, such as lane-changing dynamics, traffic signalization, and vehicle-type heterogeneity, along with operational factors including entry rates, exit probabilities, and the number of waiting vehicles. The model aims to fill a gap in existing emission studies by capturing the dynamics of heterogeneous, multi-lane systems with integrated feedback mechanisms. These considerations provide valuable insights into traffic management and emission mitigation strategies. The analysis reveals that prioritizing information feedback from the system entrance, rather than relying on feedback from the entire system, more effectively reduces traffic emissions. Additionally, the Vehicle Number Feedback Strategy (VNFS) proved to be the most effective, reducing the number of waiting vehicles and consequently lowering CO2 emissions. Furthermore, simulation results indicate that for entry rate values below approximately 0.4, asymmetrical lane-changing generates higher emissions, whereas symmetrical lane-changing yields elevated emissions when entry rate surpasses this threshold. Overall, this research contributes to advancing the understanding of traffic management strategies and offers actionable insights for emissions mitigation in two-route systems, with potential applications in intelligent transportation infrastructure. Full article
(This article belongs to the Special Issue Smart Mobility and Transportation Infrastructure)
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19 pages, 3128 KiB  
Article
Study on Shaking Table Test and Vulnerability Analysis of 220 kV Indoor Substation in High-Intensity Areas
by Jie Feng, Liuhuo Wang, Yueqing Chen, Xiaohui Wu and Dayang Wang
Infrastructures 2025, 10(5), 119; https://doi.org/10.3390/infrastructures10050119 - 13 May 2025
Viewed by 366
Abstract
This study investigates the seismic performance of the V3.0 220 kV standard-designed substation of the Southern Power Grid, located in a high-intensity seismic zone, with a focus on the application of seismic isolation technology. Seismic isolation and structural analysis were conducted and shaking [...] Read more.
This study investigates the seismic performance of the V3.0 220 kV standard-designed substation of the Southern Power Grid, located in a high-intensity seismic zone, with a focus on the application of seismic isolation technology. Seismic isolation and structural analysis were conducted and shaking table tests were performed on both isolated and non-isolated structural models. A total of 40 tests were carried out using three levels of ground motion intensity (i.e., 140 gal, 400 gal, and 800 gal) and in three directions (unidirectional, bidirectional, and triaxial). The dynamic characteristics, seismic response, and isolation effectiveness were evaluated. Results indicate that the test models exhibit strong agreement with theoretical and numerical predictions, with an average frequency deviation of 10.98%. The fundamental period of the isolated structure was extended by a factor of 2.33 compared to the non-isolated configuration. As the peak ground acceleration increased, structural frequency decreased, and the period increased. The isolated structure showed a lower first-period growth rate (4.82%) than the non-isolated structure (15.38%). Even under 800 gal excitations, the isolated structure remained within the elastic range. Seismic isolation significantly reduced structural response, with a control effectiveness exceeding 50%, enabling a one-degree reduction in seismic design intensity. A vulnerability analysis based on 200 simulated earthquake cases revealed that the isolated structure exhibited lower failure probabilities across four performance states. At 600 gal PGA, the failure probability in the LS3 state was reduced by 27.8%. These findings confirm the effectiveness and reliability of seismic isolation design for substations in high seismic intensity regions. Full article
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25 pages, 7182 KiB  
Article
Evaluation of an Approximate Seismic Assessment Procedure for Load-Bearing Masonry Buildings
by Stylianos I. Pardalopoulos, Anastasia E. Gkektsi and Vassilios A. Lekidis
Infrastructures 2025, 10(5), 118; https://doi.org/10.3390/infrastructures10050118 - 12 May 2025
Viewed by 383
Abstract
The Building Inspection Priority Index (BIPI) method for masonry structures is an approximate procedure for the preliminary assessment of the seismic capacity of existing load-bearing masonry buildings and for the prioritization of each building for conducting third-tier seismic assessments. This study investigates the [...] Read more.
The Building Inspection Priority Index (BIPI) method for masonry structures is an approximate procedure for the preliminary assessment of the seismic capacity of existing load-bearing masonry buildings and for the prioritization of each building for conducting third-tier seismic assessments. This study investigates the applicability and reliability of the BIPI method by evaluating the seismic adequacy of four existing masonry buildings, located in Epirus and Central Macedonia, Greece, and by comparing the results with the damage sustained by these buildings during previous strong earthquakes. Full article
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20 pages, 1876 KiB  
Article
Macro-Level Modeling of Traffic Crash Fatalities at the Scene: Insights for Road Safety
by Carlos Fabricio Assunção da Silva, Mauricio Oliveira de Andrade, Cintia Campos, Alex Mota dos Santos, Hélio da Silva Queiroz Júnior and Viviane Adriano Falcão
Infrastructures 2025, 10(5), 117; https://doi.org/10.3390/infrastructures10050117 - 9 May 2025
Viewed by 621
Abstract
This study applied 2019 macro-level data from DATASUS to model traffic fatalities at the scene. Ordinary least squares (OLS) and censored regression models (TOBIT) were the methodologies used to identify the significant variables explaining the occurrence of deaths on public roads due to [...] Read more.
This study applied 2019 macro-level data from DATASUS to model traffic fatalities at the scene. Ordinary least squares (OLS) and censored regression models (TOBIT) were the methodologies used to identify the significant variables explaining the occurrence of deaths on public roads due to crashes. The number of fatalities on public roadways was then modeled using a multilayer perceptron artificial neural network employing the significant variables as predictors according to the generalization capacity of complex predictive models. The OLS and TOBIT findings indicated that the variables motorcycles and scooters per capita, municipal human development index, and number of SUS emergency units were the most important for modeling traffic fatalities at the scene at the national and regional levels. Applying these variables, the neural network’s best results achieved a hit rate of 88% for Brazil and 95% for the Northeast model. The contribution of this study is providing an approach combining various methods and considering a range of variables influencing traffic fatalities at the scene. The findings offer insights for policymakers, researchers, and practitioners involved in road safety initiatives, mainly where crash data are scarce, and macro-level analysis is necessary. Full article
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29 pages, 1358 KiB  
Article
Exploring Behavioral Intentions and Sustainability Perspectives for the China–Laos High-Speed Rail Service Among Thai People: A Comparative Study of Urban and Rural Zones
by Thanapong Champahom, Dissakoon Chonsalasin, Kestsirin Theerathitichaipa, Fareeda Watcharamaisakul, Sajjakaj Jomnonkwao, Vatanavongs Ratanavaraha and Rattanaporn Kasemsri
Infrastructures 2025, 10(5), 116; https://doi.org/10.3390/infrastructures10050116 - 8 May 2025
Cited by 1 | Viewed by 2367
Abstract
The Belt and Road Initiative’s infrastructure development faces significant challenges in understanding and addressing the divergent perceptions between urban and rural populations, particularly regarding high-speed rail projects. This study investigates the behavioral intentions and sustainability perspectives regarding the China–Laos High-Speed Rail Service among [...] Read more.
The Belt and Road Initiative’s infrastructure development faces significant challenges in understanding and addressing the divergent perceptions between urban and rural populations, particularly regarding high-speed rail projects. This study investigates the behavioral intentions and sustainability perspectives regarding the China–Laos High-Speed Rail Service among Thai people, with particular focus on urban–rural differences. While the China–Laos railway became operational in December 2021, it is important to note that the high-speed rail extension into Thailand is not yet in operation and remains in the planning and development stage. Using survey data from 2866 respondents (1301 urban and 1565 rural) across 22 Thai provinces, this study employs structural equation modeling to examine relationships between perceived benefits, service quality, cultural factors, emotional aspects, and behavioral intentions. The findings reveal significant urban–rural disparities in infrastructure acceptance patterns. Urban residents demonstrate stronger relationships between perceived benefits and attitudes (β = 0.260) compared to rural residents (β = 0.170), while rural populations show substantially stronger responses to cultural factors (β = 0.365 vs. β = 0.309). Service quality more strongly influences behavioral intentions in rural areas (β = 0.154 vs. β = 0.138), suggesting varying priorities across geographical contexts. The study recommends implementing differentiated development strategies that address these urban–rural differences, including culturally sensitive rural engagement approaches and comprehensive service quality management systems. This research contributes to infrastructure development literature by empirically validating spatial heterogeneity in acceptance factors, extending theoretical frameworks on sustainability perceptions, and providing evidence-based guidance for managing urban–rural disparities in major infrastructure projects. Full article
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24 pages, 9196 KiB  
Article
Assessment of Anisotropy in Cold In-Place Recycled Materials Using Shear Wave Velocity and Computed Tomography Analysis
by Quentin Lecuru, Yannic Ethier, Alan Carter and Mourad Karray
Infrastructures 2025, 10(5), 115; https://doi.org/10.3390/infrastructures10050115 - 8 May 2025
Viewed by 468
Abstract
Pavement materials like hot mix asphalt (HMA) and cold recycled mixes (CRMs) are typically considered isotropic. This study evaluates the anisotropy of a cold in-place recycled (CIR) material using the shear wave velocity (Vs) parameter. The piezoelectric ring actuator technique (P-RAT) [...] Read more.
Pavement materials like hot mix asphalt (HMA) and cold recycled mixes (CRMs) are typically considered isotropic. This study evaluates the anisotropy of a cold in-place recycled (CIR) material using the shear wave velocity (Vs) parameter. The piezoelectric ring actuator technique (P-RAT) is utilized to assess the Vs parameter in three directions in CIR slabs. Similarly, the ultrasonic pulse velocity (UPV) technique is employed to measure P-wave velocities. Both methods evaluate mechanical properties in multiple directions. Complex modulus tests are conducted to link velocities results to |E*| modulus. Finally, computed tomography (CT) scans are performed on the specimens in order to evaluate anisotropy resulting from aggregate alignment. The Vs obtained using P-RAT and the Vp from UPV indicate anisotropy, as the wave velocities differ across the three directions. Differences range from 0.6 to 11.6% in Vs, influenced by measurement location. UPV results are analysed in relation to the |E*| modulus master curves, demonstrating that the first peak arrival time for the P-wave corresponds with the master curve. CT scan analysis reveals that the aggregates tend to be more aligned in the direction of the compacting wheel’s displacement, which also highlights anisotropy. Full article
(This article belongs to the Special Issue Sustainable and Digital Transformation of Road Infrastructures)
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41 pages, 3372 KiB  
Review
Traffic Signal Control via Reinforcement Learning: A Review on Applications and Innovations
by Panagiotis Michailidis, Iakovos Michailidis, Charalampos Rafail Lazaridis and Elias Kosmatopoulos
Infrastructures 2025, 10(5), 114; https://doi.org/10.3390/infrastructures10050114 - 6 May 2025
Cited by 1 | Viewed by 4929
Abstract
Traffic signal control plays a pivotal role in intelligent transportation systems, directly affecting urban mobility, congestion mitigation, and environmental sustainability. As traffic networks become more dynamic and complex, traditional strategies such as fixed-time and actuated control increasingly fall short in addressing real-time variability. [...] Read more.
Traffic signal control plays a pivotal role in intelligent transportation systems, directly affecting urban mobility, congestion mitigation, and environmental sustainability. As traffic networks become more dynamic and complex, traditional strategies such as fixed-time and actuated control increasingly fall short in addressing real-time variability. In response, adaptive signal control—powered predominantly by reinforcement learning—has emerged as a promising data-driven solution for optimizing signal operations in evolving traffic environments. The current review presents a comprehensive analysis of high-impact reinforcement-learning-based traffic signal control methods, evaluating their contributions across numerous key dimensions: methodology type, multi-agent architectures, reward design, performance evaluation, baseline comparison, network scale, practical applicability, and simulation platforms. Through a systematic examination of the most influential studies, the review identifies dominant trends, unresolved challenges, and strategic directions for future research. The findings underscore the transformative potential of RL in enabling intelligent, responsive, and sustainable traffic management systems, marking a significant shift toward next-generation urban mobility solutions. Full article
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13 pages, 1518 KiB  
Article
Sustainability in Infrastructure Project Management—Analysis of Two European Megaprojects
by Baowen Lou, Mahgol Afshari, Agnar Johansen, Freja Nygaard Rasmussen and Rolf André Bohne
Infrastructures 2025, 10(5), 113; https://doi.org/10.3390/infrastructures10050113 - 6 May 2025
Viewed by 898
Abstract
To implement the “Green Transition” in civil engineering, this study provides a new critical perspective analyzing the sustainability measures adopted by two European megaprojects. Government regulations and legislation, reward mechanism, technological innovations, the carbon evaluation system as well as tracking and monitoring systems [...] Read more.
To implement the “Green Transition” in civil engineering, this study provides a new critical perspective analyzing the sustainability measures adopted by two European megaprojects. Government regulations and legislation, reward mechanism, technological innovations, the carbon evaluation system as well as tracking and monitoring systems are further discussed in this research to manage megaprojects in a more sustainable way. Document reviews, field trips (both exhibition area and construction sites), and in-depth interviews with relevant stakeholders were conducted regarding two European megaprojects, namely the A16 Ring Road in the Netherlands and Fehmarnbelt Tunnel in Denmark, when it comes to sustainability transitions. Notwithstanding the regional limitations of the selected case studies, the results illustrate that the implemented policies and regulations promote the sustainability transitions in projects and lead to environmental and societal benefits. Among the others, the requirement to quantify the carbon emissions is a central step during the tendering and execution phases of the megaprojects. Future studies need to comprehensively address the challenges related to project management and sustainable transitions as well as delve into other possible practices implemented locally in different locations. Local policies and regulations, innovation in technology and materials as well as the quantification of environmental impacts are key aspects to accelerate such change towards carbon neutrality. Full article
(This article belongs to the Special Issue Sustainable Materials, Design, Maintenance, and Management for Transport Infrastructure)
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31 pages, 7185 KiB  
Article
A Deep Reinforcement Learning Framework for Last-Mile Delivery with Public Transport and Traffic-Aware Integration: A Case Study in Casablanca
by Amine Mohamed El Amrani, Mouhsene Fri, Othmane Benmoussa and Naoufal Rouky
Infrastructures 2025, 10(5), 112; https://doi.org/10.3390/infrastructures10050112 - 3 May 2025
Viewed by 1112
Abstract
Optimizing last-mile delivery operations is an essential component in making a modern city livable, particularly in the face of rapid urbanization, increasing e-commerce activity, and the growing demand for fast deliveries. These factors contribute significantly to traffic congestion and pollution, especially in densely [...] Read more.
Optimizing last-mile delivery operations is an essential component in making a modern city livable, particularly in the face of rapid urbanization, increasing e-commerce activity, and the growing demand for fast deliveries. These factors contribute significantly to traffic congestion and pollution, especially in densely populated urban centers like Casablanca. This paper presents an innovative approach to optimizing last-mile delivery by integrating public transportation into the logistics network to address these challenges. A custom-built environment is developed, utilizing public transportation nodes as transshipment nodes for standardized packets of goods, combined with a realistic simulation of traffic conditions through the integration of the travel time index (TTI) for Casablanca. The pickup and delivery operations are optimized with the proximal policy optimization algorithm within this environment, and experiments are conducted to assess the effectiveness of public transportation integration and three different exploration strategies. The experiments show that scenarios integrating public transportation yield significantly higher mean rewards—up to 1.49 million—and more stable policy convergence, compared to negative outcomes when public transportation is absent. The highest-performing configuration, combining PPO with segmented training and public transport integration, achieves the best value loss (0.0129) and learning stability, albeit with a trade-off in task completion. This research introduces a novel, scalable reinforcement learning framework to optimize pickup and delivery with time windows by exploiting both public transportation and traditional delivery vehicles. Full article
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24 pages, 8795 KiB  
Article
Analysis and Classification of Distress on Flexible Pavements Using Convolutional Neural Networks: A Case Study in Benin Republic
by Crespin Prudence Yabi, Godfree F. Gbehoun, Bio Chéissou Koto Tamou, Eric Alamou, Mohamed Gibigaye and Ehsan Noroozinejad Farsangi
Infrastructures 2025, 10(5), 111; https://doi.org/10.3390/infrastructures10050111 - 29 Apr 2025
Viewed by 520
Abstract
Roads are critical infrastructure in multi-sectoral development. Any country that aims to expand and stabilize its activities must have a network of paved roads in good condition. However, that is not the case in many countries. The usual methods of recording and classifying [...] Read more.
Roads are critical infrastructure in multi-sectoral development. Any country that aims to expand and stabilize its activities must have a network of paved roads in good condition. However, that is not the case in many countries. The usual methods of recording and classifying pavement distress on the roads require a lot of equipment, technicians, and time to obtain the nature and indices of the damage to estimate the roadway’s quality level. This study proposes the use of pavement distress detection and classification models based on Convolutional Neural Networks, starting from videos taken of any asphalt road. To carry out this work, various routes were filmed to list the degradations concerned. Images were extracted from these videos and then resized and annotated. Then, these images were used to constitute several databases of road damage, such as longitudinal cracks, alligator cracks, small potholes, and patching. Within an appropriate development environment, three Convolutional Neural Networks were developed and trained on the databases. The accuracy achieved by the different models varies from 94.6% to 97.3%. This accuracy is promising compared to the literature models. This method would make it possible to considerably reduce the financial resources used for each road data campaign. Full article
(This article belongs to the Section Infrastructures Inspection and Maintenance)
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19 pages, 4587 KiB  
Article
Zinc Oxide as a Filler in a Hot-Mix Asphalt: Impact on Mechanical Properties
by Hugo Alexander Rondón-Quintana, Karem Tatiana Forero-Rubiano, Yohan Sebastián Valderrama-Agudelo, Juan Gabriel Bastidas-Martínez and Carlos Alfonso Zafra-Mejía
Infrastructures 2025, 10(5), 110; https://doi.org/10.3390/infrastructures10050110 - 29 Apr 2025
Viewed by 482
Abstract
Zinc oxide (ZnO) exhibits promising thermochemical properties when used as an asphalt binder modifier. Its micrometric size further enhances its potential as a substitute for natural fillers (NFs) in hot-mix asphalt (HMA). This study evaluates the effect of partially and fully replacing NFs [...] Read more.
Zinc oxide (ZnO) exhibits promising thermochemical properties when used as an asphalt binder modifier. Its micrometric size further enhances its potential as a substitute for natural fillers (NFs) in hot-mix asphalt (HMA). This study evaluates the effect of partially and fully replacing NFs with ZnO on the mechanical performance of HMA, addressing a research gap since the influence of ZnO as a filler in asphalt mixtures has not been previously investigated. NFs were replaced by ZnO at weight-based proportions of ZnO/NF = 25, 50, 75, and 100%. Initially, the morphology of NF and ZnO particles was analyzed using Scanning Electron Microscopy (SEM). Asphalt mastics were then produced with the same ZnO/NF proportions and subjected to conventional characterization tests, including penetration, softening point, and viscosity. In the next phase, HMA samples were designed using the Marshall method, incorporating ZnO at 0, 25, 50, and 100% replacement levels (designated as Control, HMA-25, HMA-50, and HMA-100, respectively). The mechanical performance of these mixtures was assessed through indirect tensile strength (ITS) and Cantabro tests. Based on the initial results, further evaluations were conducted on the Control, HMA-50, and HMA-100 mixtures to determine their resilient modulus, fatigue behavior under stress-controlled conditions, and resistance to permanent deformation (static creep test). The findings indicate that ZnO can replace NF in HMA without compromising Marshall stability or Cantabro strength. Additionally, ZnO-modified HMAs exhibit increases in stiffness under cyclic loading, and improvements in resistance to permanent deformation, fatigue performance, and moisture damage. These enhancements occur despite a 0.5% reduction in binder content compared to the Control HMA and a slight increase in porosity. Full article
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19 pages, 6569 KiB  
Article
The Long-Term Inspection and Monitoring of Transition Zones with a Sudden Change in Railway Track Stiffness
by Stanislav Hodas, Jana Izvoltova and Erik Vrchovsky
Infrastructures 2025, 10(5), 109; https://doi.org/10.3390/infrastructures10050109 - 28 Apr 2025
Viewed by 611
Abstract
Transition zones are located at points on a track where there has been a change in the main composition of the railway infrastructure; as such, there are many sections that undergo a sudden change in the stiffness of the structures built. When trains [...] Read more.
Transition zones are located at points on a track where there has been a change in the main composition of the railway infrastructure; as such, there are many sections that undergo a sudden change in the stiffness of the structures built. When trains are running, a longitudinal shockwave is created by the wheels, hitting these building objects with a greater stiffness and deforming the surroundings of these zones. The greatest amount of attention should be paid to the transition points from the fixed track to the classic track with a track bed, including objects of the railway substructure, such as bridges and portals of tunnels. As part of the research on the main corridor lines, long-term inspection and monitoring studies were carried out using a trolley with a continuous measurement system; height changes in the deflections of rails are evidence of their behaviour. The measurements took place on a fixed track and a track with ballast. The changes in the height jumps between the fixed railway track and the track with a gravel bed are significant. These height deflections allow designers to develop new, more durable construction designs. Full article
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17 pages, 3826 KiB  
Article
Empirical Investigation of the Effects of the Measurement-Data Size on the Bayesian Structural Model Updating of a High-Speed Railway Bridge
by Kodai Matsuoka, Haruki Yotsui and Kiyoyuki Kaito
Infrastructures 2025, 10(5), 108; https://doi.org/10.3390/infrastructures10050108 - 25 Apr 2025
Viewed by 433
Abstract
Bayesian structural model updating (SMU) is among the most powerful methods for estimating the bending stiffness and modal damping of high-speed railway (HSR) bridges and predicting their bridge-response-based resonance responses. Although studies indicated that the convergence to the true value as the observed [...] Read more.
Bayesian structural model updating (SMU) is among the most powerful methods for estimating the bending stiffness and modal damping of high-speed railway (HSR) bridges and predicting their bridge-response-based resonance responses. Although studies indicated that the convergence to the true value as the observed data increase favored Bayesian inference, the data-size effects on the estimation accuracy have not been sufficiently investigated. Here, the maximum bridge deck acceleration upon the passage of a train, which is used in European bridge designs, is explored, and the data-size effect on the Bayesian SMU is empirically investigated. For an HSR bridge spanning approximately 50 m, the parameters and maximum acceleration of a beam model on which the moving loads act are updated by the Markov chain Monte Carlo simulation method using the measured maximum acceleration. A comparison of the estimated values with different measurement data revealed that the estimated values converged for three samples, when the data included the resonance state of the test bridge. Overall, the results can be employed to establish a logical method for determining the necessary field measurement specifications for ensuring the accuracy of Bayesian SMU. Full article
(This article belongs to the Section Infrastructures Inspection and Maintenance)
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20 pages, 5670 KiB  
Article
Performance Evaluation of Waste Rubber-Modified Asphalt Mixtures: A Comparative Study of Asphalt Concrete and Stone Mastic Asphalt Gradings
by Ivana Ban, Ivana Barišić, Marijana Cuculić and Matija Zvonarić
Infrastructures 2025, 10(5), 107; https://doi.org/10.3390/infrastructures10050107 - 25 Apr 2025
Viewed by 666
Abstract
Crumb rubber (CR) obtained from end-of-life tyres (ELT) has gained significant attention in the sustainable design of asphalt pavements in recent years, showing a promising perspective in the enhancement of pavement performance related to its structural and functional properties. Existing research on CR [...] Read more.
Crumb rubber (CR) obtained from end-of-life tyres (ELT) has gained significant attention in the sustainable design of asphalt pavements in recent years, showing a promising perspective in the enhancement of pavement performance related to its structural and functional properties. Existing research on CR influence on pavement performance mostly focused on peculiarities of asphalt mixture modification procedures—dry and wet processes, CR content in the mixture and CR particle size. In this study, a laboratory-based experimental investigation of CR effect on two different mixture gradations, namely dense-graded and gap-graded mixtures with three different binder contents, was performed. CR was added in mixtures through binder modification, with a constant CR content of 18% by binder weight in all mixtures. Volumetric properties—maximum mixture density, bulk density and void characteristics, alongside mechanical properties determined by the Marshall test method—were determined on unmodified and modified mixtures. The goal was to evaluate the influence of CR modification with respect to three different binder contents. The results showed that gap-graded mixtures are more sensitive to change in CR modified binder content in comparison to dense-graded mixtures in terms of air voids content. Furthermore, the mechanical properties of CR-modified mixtures were slightly enhanced in gap-graded mixtures, showing a promising potential of CR modification for pavement performance. However, the choice of optimal binder content in CR-modified mixtures was shown to be a critical mixture design parameter due to the increased sensitivity of binder content change to the analysed voids properties and permanent deformations. Full article
(This article belongs to the Special Issue Sustainable Road Design and Traffic Management)
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33 pages, 10634 KiB  
Review
UAV Applications for Monitoring and Management of Civil Infrastructures
by Alberto Villarino, Hugo Valenzuela, Natividad Antón, Manuel Domínguez and Ximena Celia Méndez Cubillos
Infrastructures 2025, 10(5), 106; https://doi.org/10.3390/infrastructures10050106 - 24 Apr 2025
Cited by 1 | Viewed by 1760
Abstract
Civil engineering is a field of knowledge in direct contact with the citizen, not only in the design and construction of infrastructure but also in its maintenance, conservation, monitoring, and management. The integration of new technologies, such as drones, is revolutionizing work methodologies, [...] Read more.
Civil engineering is a field of knowledge in direct contact with the citizen, not only in the design and construction of infrastructure but also in its maintenance, conservation, monitoring, and management. The integration of new technologies, such as drones, is revolutionizing work methodologies, offering new possibilities for the execution and management of infrastructure and minimizing human intervention in these jobs, with the increase in occupational safety and cost reduction that this entails. This study presents a comprehensive review of the literature on UAV applications for the monitoring and management of civil infrastructure. The applicability of UAVs and their connection with the main existing sensors and technologies are analyzed, such as visible cameras (RGB), multispectral cameras, and hyperspectral cameras, in the most relevant areas of civil engineering, such as building inspection, bridge inspection, dams, power line inspection, photovoltaic plants, inspection, hydrological studies road inspection, slope supervision, and the maintenance and monitoring of landfill operation. The impact and scope of these technologies are addressed, as well as the benefits in terms of process automation, efficiency, safety, and cost reduction. The incorporation of drones promises to significantly transform the practice of civil engineering, improving the sustainability and resilience of infrastructures. Full article
(This article belongs to the Section Infrastructures Inspection and Maintenance)
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29 pages, 1438 KiB  
Review
A Comparison of Return Periods of Design Ground Motions for Dams from Different Agencies and Organizations
by Kevin Zeh-Zon Lee, David R. Gillette and Angel Gutierrez
Infrastructures 2025, 10(5), 105; https://doi.org/10.3390/infrastructures10050105 - 24 Apr 2025
Viewed by 800
Abstract
The purpose of this paper is to review and compare the criteria of seismic design ground motions and approaches in seismic hazard analysis set forth by various agencies and organizations. A total of 13 agencies and organizations were reviewed including three for non-dam [...] Read more.
The purpose of this paper is to review and compare the criteria of seismic design ground motions and approaches in seismic hazard analysis set forth by various agencies and organizations. A total of 13 agencies and organizations were reviewed including three for non-dam structures. It was found the both the deterministic and probabilistic seismic hazard analysis approaches have been used. Many have combined the two approaches to complement each other. High-consequence dams are designed for a long ground motion return period of approximately 10,000 years, which lies between the design return periods of bridges and nuclear power plants. In contrast to other agencies and organizations, U.S. Bureau of Reclamation dams are not subjected to specific design return periods; they are designed based on risk-informed decisions, which consider the failure probability in relation to the public protection guideline values. In addition, criteria from the Reclamation Design Standards are to be followed in any dam modifications. Based on the findings of this paper, it was deemed that the current Reclamation dam safety decisions and practices are in general agreement with other dam agencies and organizations that also adopt the risk-informed decision process. Full article
(This article belongs to the Special Issue Advances in Dam Engineering of the 21st Century)
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50 pages, 3238 KiB  
Systematic Review
Industry 4.0 Technologies for Sustainable Transportation Projects: Applications, Trends, and Future Research Directions in Construction
by Behzad Abbasnejad, Sahar Soltani, Alireza Ahankoob, Sakdirat Kaewunruen and Ali Vahabi
Infrastructures 2025, 10(5), 104; https://doi.org/10.3390/infrastructures10050104 - 22 Apr 2025
Viewed by 1532
Abstract
This study presents a mixed-method systematic literature review (SLR) investigating the applications of Industry 4.0 (I4.0) technologies for enhancing sustainability in transportation infrastructure projects from a construction perspective. A corpus of 199 scholarly articles published between 2009 and November 2023 was meticulously selected [...] Read more.
This study presents a mixed-method systematic literature review (SLR) investigating the applications of Industry 4.0 (I4.0) technologies for enhancing sustainability in transportation infrastructure projects from a construction perspective. A corpus of 199 scholarly articles published between 2009 and November 2023 was meticulously selected from the Scopus database. The thematic analysis categorised the publications into four main clusters: infrastructure type, technology types, project lifecycle stages, and geographic context. The scientometric analysis revealed a burgeoning interest in the integrating of I4.0 technologies to enhance sustainability—particularly environmental sustainability. Among these, Building Information Modelling (BIM)-related tools emerged as the most extensively studied domain (33.50%), followed by the Internet of Things (IoT) and sensors (14%), and Artificial Intelligence (AI) (13.22%). The findings demonstrate that roads, highways, and bridges are the most studied infrastructure types, with BIM being predominantly utilised for energy assessment, sustainable design, and asset management. The main contributions of this review are threefold: (1) providing a comprehensive framework that categorises I4.0 applications and their sustainability impacts across transportation infrastructure types and project lifecycle stages, (2) identifying key technical challenges in integrating I4.0 technologies with sustainability assessment tools, and (3) revealing underexplored areas and providing clear directions for future research. The findings provide actionable insights for researchers and industry practitioners aiming to adopt integrated, sustainability-driven digital approaches in transport infrastructure delivery. Full article
(This article belongs to the Special Issue Emerging Technologies for Effective and Intelligent Transport Infrastructure Monitoring)
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29 pages, 6754 KiB  
Article
Assessing Drainage Infrastructure in Coastal Lowlands: Challenges, Design Choices, and Environmental and Urban Impacts
by Beatriz Cruz Amback, Paula Morais Canedo de Magalhães, Luiz Eduardo Siqueira Saraiva, Matheus Martins de Sousa and Marcelo Gomes Miguez
Infrastructures 2025, 10(5), 103; https://doi.org/10.3390/infrastructures10050103 - 22 Apr 2025
Cited by 1 | Viewed by 588
Abstract
Urban flooding is a growing concern, particularly in coastal lowland cities where climate change exacerbates hazards through rising sea levels and intense rainfall. Traditional flood defenses like fluvial polders often exacerbate urban fragmentation and maintenance costs if poorly integrated into planning. This study [...] Read more.
Urban flooding is a growing concern, particularly in coastal lowland cities where climate change exacerbates hazards through rising sea levels and intense rainfall. Traditional flood defenses like fluvial polders often exacerbate urban fragmentation and maintenance costs if poorly integrated into planning. This study proposes a multifunctional assessment design framework to evaluate polder design effectiveness considering both the hydraulic and social–environmental dimensions, emphasizing blue–green infrastructure (BGI) for flood control, leisure, and landscape integration. Three design scenarios for Rio de Janeiro’s Jardim Maravilha neighborhood were modeled hydrodynamically: S1 (dike near urban areas, pump-dependent) and S2/S3 (dikes along the riverbank, gravity-driven). Results show S2/S3 outperformed S1 in storage capacity (2.7× larger volume), freeboard resilience (0.42–0.43 m vs. 0.25 m), and urban integration (floodable parks accessible to communities), though S1 had faster reservoir emptying. Under climate change, all scenarios sustained functionality, but S1’s freeboard reduced by 86%, nearing its limit. The framework’s standardized scoring system balanced quantitative and qualitative criteria, revealing trade-offs between hydraulic efficiency and urban adaptability. The optimized S3 design, incorporating external storage and dredging, achieved the best compromise. This approach aids decision-making by systematically evaluating resilience, operational feasibility, and long-term climate adaptation, supporting sustainable flood infrastructure in coastal cities. Full article
(This article belongs to the Special Issue Smart, Sustainable and Resilient Infrastructures, 3rd Edition)
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15 pages, 19750 KiB  
Article
Research on Intelligent Identification Technology for Bridge Cracks
by Yumeng Su, Yunlong Song, Zhaomin Zhan, Zhuo Bi, Bang Zhou, Youling Yu and Yanting Song
Infrastructures 2025, 10(5), 102; https://doi.org/10.3390/infrastructures10050102 - 22 Apr 2025
Viewed by 673
Abstract
The infrastructure construction of bridges is growing rapidly, and the quality of concrete structures is becoming increasingly stringent. However, the issue of cracks in concrete structures remains prominent. In on-site bridge crack detection, the traditional crack identification techniques fail to meet demands due [...] Read more.
The infrastructure construction of bridges is growing rapidly, and the quality of concrete structures is becoming increasingly stringent. However, the issue of cracks in concrete structures remains prominent. In on-site bridge crack detection, the traditional crack identification techniques fail to meet demands due to their inefficiency, inaccuracy, and the challenges posed by high-altitude conditions. In response to this, this paper proposes a bridge crack multi-task integration algorithm based on YOLOv8 object detection and DeepLabv3+ semantic segmentation. This integrated approach offers advantages such as high precision and low inference time. Testing wall cracks using this method, compared to the original approach, resulted in a 10.18% improvement in IOU and a 9.64% improvement in the F1 score. Regarding the detection model, it was deployed on edge computing devices. By applying the TensorRT inference acceleration framework, the camera FPS increased to 9.66, a 59.97% improvement compared to the version without the acceleration framework. This enabled accurate, real-time bridge crack detection on the edge computing devices. Furthermore, the edge computing device was also applied in a self-developed drone, which was tested on-site at the Donghai Bridge, providing a new solution for safe and reliable structural inspection. Full article
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