Infrastructures

The longevity and safety of asphalt pavements, which form the foundation of our transportation infrastructure, are directly impacted by their performance. Pavement performance has traditionally been measured using the Marshall Mix Design method, which is a time- and resource-intensive laboratory procedure. Machine learning algorithms (MLAs) are increasingly popular today and are being utilized in various fields. Their performances vary; therefore, evaluating different MLAs and comparing them is important. The potential of various machine learning (ML) algorithms to predict Marshall Stability (MS) and Marshall Flow (MF) was investigated in this work. We collected data from published studies in the literature encompassing 732 data points to train and evaluate ML models. Eight key input parameters were considered for modeling. We used three feature importance analysis techniques (Random Forest, Permutation Importance, and Lasso Regression) to determine which parameters were the most significant. Linear regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machines (SVMs), Gradient Boosting Machines (GBMs), and Artificial Neural Networks (ANNs) were the six MLAs that were assessed. Robust statistical measures such as MSE, MAE, R², and RMSE were employed to evaluate each model’s performance. Our results indicate that the RF algorithm had the best performance for both MS and MF parameter prediction, followed by ANN and DT. The predicted and actual values showed a strong correlation, which was evidenced by the high R² and the lowest values in other error metrics, indicating good performance. This highlights the significance of selecting an optimal machine learning algorithm for a particular predictive task. Full article

This study focuses on the evolution of the Arousa Island Bridge, a critical infrastructure connecting, in northwestern Spain, the Arousa island to the Galician coast. Since its commissioning in 1985, the bridge has experienced damage due to corrosion, culminating in a major repair intervention in 2011 using hybrid galvanic cathodic protection. This repair was essential in addressing identified pathologies and ensuring the safety of the structure. In 2021, additional repairs needed to be completed, and a thorough study and testing campaign was conducted in 2023 which included the extraction of zinc anode samples from the bridge. The present work evaluates the effectiveness of the repair measures implemented since the intervention, with particular attention to corrosion risk and the durability of the cathodic protection system installed to mitigate corrosion risks in the reinforced concrete exposed to a harsh marine environment. A key aspect of this study is the correlation established between the indirect measurements utilized to evaluate zinc consumption within the cathodic protection system and the direct assessment obtained from the extraction of the anodes, which provides a tangible measure of this consumption. The calculated service life was updated with the measurement, and the integrity of the system was assessed. Full article

The detection of voids in pavement infrastructure is essential for road safety and efficient maintenance. Traditional methods of analyzing ground-penetrating radar (GPR) data are labor-intensive and error-prone. This study presents a novel approach using the EfficientDet-D3 deep learning model for automated void detection in GPR images. The model combines advanced feature extraction and compound scaling to balance accuracy and computational efficiency, making it suitable for real-time applications. A diverse GPR image dataset, including various pavement types and environmental conditions, was curated and preprocessed to improve model generalization. The model was fine-tuned through hyperparameter optimization, achieving a precision of 91.2%, a recall of 87.5%, and an F1-score of 89.3%. It also attained mean Average Precision (mAP) values of 89.7% at IoU 0.5 and 84.3% at IoU 0.75, demonstrating strong localization performance. Comparative analysis with models such as YOLOv8 and Mask R-CNN shows that EfficientDet-D3 offers a superior balance between accuracy and inference speed, with an inference time of 68 ms. This research provides a scalable, efficient solution for pavement void detection, paving the way for integrating deep learning models into pavement management systems to enhance infrastructure sustainability. Future work will focus on model optimization and expanding dataset diversity. Full article

Inaccurate cost and schedule estimations in road infrastructure projects continue to be a critical source of contractual disputes and financial inefficiencies, particularly in developing countries. While quantitative risk analysis (QRA) methods such as Monte Carlo simulation (MCS) and schedule risk analysis (SRA) are well-established in the literature, their practical adoption remains limited in contexts with low technical capacity and limited access to advanced modeling tools. This study addresses this gap by proposing a practical and accessible quantitative risk analysis framework tailored to the needs of professionals with limited expertise in probabilistic techniques. The framework combines MCS and SRA using probability distributions (PERT, triangular, and normal) and was empirically validated through three road projects in Peru. Results indicated substantial reductions in uncertainty, achieving cost contingency estimates between 1.34% and 11% which were significantly lower than documented overruns of up to 32.29%. Schedule contingencies ranged from 28.71% to 91.67%, markedly improving accuracy. The novelty of this research lies in its context-adapted implementation strategy, offering a robust and easily replicable approach for similar infrastructure environments in Latin America and beyond. This contribution bridges the gap between theoretical risk modeling and its practical adoption, thus enhancing the reliability of infrastructure planning under resource-constrained conditions. Full article

Natural water content affects many geotechnical parameters and geological properties of soils, which can reduce cohesion and friction, leading to potential failures in structures such as foundations, retaining walls, and slopes. Identification of the water content helps in designing effective drainage and water management systems to prevent flooding and erosion. In tunnel engineering, soil water content plays an important role as the stability of the tunnel face depends on it. This research solves the problem of classifying soil images depending on the natural water content by computer vision technology. First, laboratory soil tests were carried out, and the relationship between the amount of torque on the screw conveyor and the moisture content of the soil was established; photographs of the soil at different conditions were taken at each step of the experiment. Second, the resulting dataset after preprocessing was processed by convolutional neural network algorithms during deep learning; the transfer learning technique was used to obtain better results. As a result, seven algorithms were obtained that allow classifying the soil images, which can later be used to optimize the tunnel construction process. The best classification ability is demonstrated by the algorithm based on the DenseNet architecture (accuracy 0.9302 and loss 0.1980). The proposed model surpasses traditional approaches due to its increased automation and processing speed. Laboratory tests can be carried out only once for one type of soil in order to determine the boundaries of water content for classes labeling, after which only a cheap camera is required from the equipment to transmit new images for processing by the algorithm. Full article

The prevalence of car dependence and sedentary lifestyles has created concern in the transportation and health sectors. Walking is the most popular and practical kind of exercise that can significantly enhance health. In Chile, more than half of older adults have health issues and almost 72% of the elderly population never engages in physical activity. This study aims to investigate the relationship between older adults’ walking behavior and the built environment along the streets and parks in Santiago’s middle-income neighborhoods. Six medium-income central and pericentral neighborhoods of Santiago were selected. The average number of older persons who walk along the paths and two modified audit forms were used to measure walking behavior and built environment features, respectively. Both correlation analysis and backward regression were used to examine the associations. While elements like the existence of bus stops, pedestrian streets, and general cleanliness contribute to the enhanced number of older adults who walk along street segments, the presence of insecurity signs was found to be negatively associated with the number of older adults who walk in the neighborhood parks. Furthermore, complexity and mystery showed a negative association with the number of older adults in the neighborhood parks. Urban policymakers might use these findings to encourage older adults to walk more in Santiago’s medium-income neighborhoods. Full article

The sloshing in storage tanks can exert negative influences on the safety and stability of tank structures undergoing earthquake excitation. An analytical mechanical model is presented here to investigate the seismic responses of a base-isolated cylindrical tank resting on soil. The continuous liquid sloshing is modeled as the convective spring–mass, the impulsive spring–mass, and the rigid mass. The soil impedances are equivalent to the systematic lumped-parameter models. The bearing isolation is considered as the elastic–viscous damping model. A comparison between the present and reported results is presented to prove the accuracy of the coupling model. A parametric analysis is carried out for base-isolated broad and slender tanks to examine the effects of the isolation period, isolation damping ratio, tank aspect ratio, and soil stiffness on structural responses. The results show that the interaction between soft soil and the base-isolated tank exerts significant influence on earthquake responses. Full article

This study addresses the critical environmental challenge of increasing carbon emissions from Thailand’s freight transport sector, focusing on container movement in the strategic Lat Krabang ICD–Laem Chabang Port corridor. The research quantifies and compares CO₂ emissions between rail and road container transport modes to identify potential carbon reduction strategies. A comprehensive activity-based methodology was employed, incorporating fuel consumption testing across multiple load conditions, detailed transport activity mapping, and the application of locally relevant emission factors. The results demonstrate that rail transport produces 32.82 kgCO₂eq/TEU compared to 53.13 kgCO₂eq/TEU for road transport, representing a 38.23% emission advantage. Fuel consumption testing revealed a power relationship between train weight and fuel consumption (y = 0.1121x^0.5147, R² = 0.97), indicating improving efficiency with increased loading. Terminal operations contribute significantly to rail transport’s emission profile, accounting for 36% of total emissions. The current modal split presents substantial opportunities for emission reduction through increased rail utilization. This study identifies and evaluates practical carbon reduction strategies across operational, technological, and policy dimensions, with priority interventions including load factor optimization, terminal efficiency improvements, locomotive modernization, and differential road pricing. This research contributes empirical evidence to support sustainable freight transport development in Thailand while establishing a methodological framework applicable to emission assessments in similar corridors throughout developing economies. Full article

Pile foundations are commonly used to support structures subjected to complex loading conditions. In seismic-prone regions, understanding the soil–pile interaction under cyclic loading is essential for ensuring the stability and safety of these foundations. Numerical modeling is an effective tool for predicting the nonlinear behavior of soil under seismic excitation, but selecting an appropriate constitutive model remains a significant challenge. This study investigates the seismic behavior of pile groups embedded in soft clay using advanced finite element analysis. The piles are modeled as aluminum with a linear elastic response and are analyzed within a soil domain characterized by two kinematic hardening constitutive models based on the Von Mises failure criterion. Model parameters are calibrated using a combination of experimental and numerical data. The study also examines the influence of pile spacing within the group on seismic response, revealing notable differences in the response patterns. The results show that the nonlinear kinematic hardening model provides a more accurate correlation with experimental centrifuge test results compared to the multilinear model. These findings contribute to enhancing the understanding of soil–pile interaction under seismic loading and improving the design of pile foundations. Full article

Bridges are assets in every society, and their deterioration can have severe economic, social, and environmental consequences. Therefore, implementing effective asset management strategies is crucial to ensure bridge infrastructure’s long-term performance and safety. Roadmaps can serve as valuable tools for bridge asset managers, helping bridge engineers make informed decisions that enhance bridge safety while maintaining controlled life cycle costs. Although some bridge asset management roadmaps exist, such as the one published by the United States Federal Highway Administration (FHWA), there is a lack of structured research roadmaps that are both region-specific and adaptable as guiding frameworks for similar studies. For instance, the FHWA roadmap cannot be universally applied across diverse regional contexts. This study addresses this critical gap by developing a research roadmap tailored to Idaho, USA. The roadmap was developed using a three-phase methodological approach: (1) a comprehensive analysis of past and ongoing Department of Transportation (DOT)-funded research projects over the last five years, (2) a nationwide survey of DOT funding and research practices, and (3) a detailed assessment of Idaho Transportation Department (ITD) deficiently rated bridge inventory, including individual element condition states. In the first phase, three filtering stages were implemented to identify the top 25 state projects. A literature review was conducted for each project to provide ITD’s Technical Advisory Committee (TAC) members with insights into research undertaken by various state DOTs. Moreover, in the second phase, approximately six questionnaires were designed and distributed to other state DOTs. These questionnaires primarily covered topics related to bridge research priorities and funding allocation. In the final phase, a condition state analysis was conducted using data-driven methods. Key findings from this three-phase methodological approach highlight that ultra-high-performance concrete (UHPC), bridge deck preservation, and maintenance strategies are high-priority research areas across many DOTs. Furthermore, according to the DOT responses, funding is most commonly allocated to projects related to superstructure and deck elements. Finally, ITD found that the most deficient elements in Idaho bridges are reinforced concrete abutments, reinforced concrete pile caps and footings, reinforced concrete pier walls, and movable bearing systems. These findings were integrated with insights from ITD’s TAC to generate a prioritized list of 23 high-impact research topics aligned with Idaho’s specific needs and priorities. From this list, the top six topics were selected for further investigation. By adopting this strategic approach, ITD aims to enhance the efficiency and effectiveness of its bridge-related research efforts, ultimately contributing to safer and more resilient transportation infrastructure. This paper could be a helpful resource for other DOTs seeking a systematic approach to addressing their bridge research needs. Full article

The optimisation of road construction planning and design prioritises safety, comfort, cost-effectiveness, and sustainability by aligning with sustainable development goals (SDGs) and integrating life cycle assessment (LCA)-based criteria. Asphalt mixture compaction is a critical construction-phase process that requires careful monitoring due to its significant impact on fuel consumption, CO₂ emissions, and pavement performance. However, characterising the compaction process during the design stage is challenging due to the unavailability of primary data, such as the compaction energy applied by the roller on-site. This study addresses this gap by developing a methodology for deriving compaction-energy-related data at the laboratory stage. An algorithm is proposed to estimate key compaction parameters, specifically the locking point and compaction curves, based on aggregate grading. Equations to improve the design of bituminous mixtures based on compaction targets were derived. The findings support more sustainable planning, the optimised selection of construction equipment, and improved competitive equilibria between different pavement technologies by promoting low-carbon and energy-efficient strategies aligned with SDGS. Full article

The paper innovatively studies the impact of dual randomness of structural parameters and seismic excitation on the seismic reliability of highway pile–slab structures using the probability density evolution method. A nonlinear stochastic dynamic model was established through the platform, integrating, for the first time, the randomness of concrete material properties and seismic motion variability. The main findings include the following: Under deterministic seismic input, the displacement angle fluctuation range caused by structural parameter randomness is ±3%, and reliability decreases from 100% to 65.26%. For seismic excitation randomness, compared to structural parameter randomness, reliability at the 3.3% threshold decreases by 7.99%, reaching 92.01%. Dual randomness amplifies the variability of structural response, reducing reliability to 86.38% and 62%, with a maximum difference of 20.5% compared to single-factor scenarios. Compared to the Monte Carlo method, probability density evolution shows significant advantages in computational accuracy and efficiency for large-scale systems, revealing enhanced discreteness and irregularity under combined randomness. This study emphasizes the necessity of addressing dual randomness in seismic design, advancing probabilistic seismic assessment methods for complex engineering systems, thereby aiding the design phase in enhancing facility safety and providing scientific basis for improved design specifications. Full article

This study examines the dynamic response of bottom-sitting steel shell structures subjected to underwater shock waves. A computational framework integrating the Arbitrary Lagrangian Eulerian (ALE) method was implemented in finite-element analysis to simulate three-dimensional interactions between shock waves and curved shell geometries (hemispherical and cylindrical configurations). An analysis of the impacts of shock-wave propagation media, explosive distance, charge equivalence, hydrostatic pressure, and shell thickness on the dynamic response of these bottom-sitting shell structures is conducted. The findings reveal that the deformation of semi-spherical steel shells subjected to underwater shock waves is significantly greater than that of shells subjected to air shock waves, with effective stress reaching up to 831.4 MPa underwater. The mechanical deformation of curved steel shells exhibits a gradual increase with increasing explosive equivalents. The center displacement of the hemispherical shell at 800 kg equivalent is 6 times that at 50 kg equivalent. Within the range of 0 to 2.0092 MPa, hydrostatic pressure leads to an approximate 26.34% increase in the center vertical displacement of the semi-cylindrical shell compared with 0 MPa, while restricting horizontal convex deformation. Increasing thickness from 0.025 m to 0.05 m results in a reduction of approximately 60% in the center vertical displacement of the semi-cylindrical shell. These quantitative correlations provide critical benchmarks for enhancing the blast resilience of underwater foundation systems. Full article

Visual inspections of concrete infrastructure in low-light environments require external lighting to ensure adequate visibility. Directional lighting sources, where an image scene is illuminated with an angled lighting source from one direction, can enhance the visibility of surface defects in an image. This paper compares directional and diffused scene illumination images for pixel-level concrete crack segmentation. A novel directional lighting image segmentation algorithm is proposed, which applies crack segmentation image processing techniques to each directionally lit image before combining all images into a single output, highlighting the extremities of the defect. This method was benchmarked against two diffused lighting crack detection techniques across a dataset with crack widths typically ranging from 0.07 mm to 0.4 mm. When tested on cracked and uncracked data, the directional lighting method significantly outperformed other benchmarked diffused lighting methods, attaining a 10% higher true-positive rate (TPR), 12% higher intersection over union (IoU), and 10% higher F1 score with minimal impact on precision. Further testing on only cracked data revealed that directional lighting was superior across all crack widths in the dataset. This research shows that directional lighting can enhance pixel-level crack segmentation in infrastructure requiring external illumination, such as low-light indoor spaces (e.g., tunnels and containment structures) or night-time outdoor inspections (e.g., pavement and bridges). Full article

The monolithic composite action of structures relies on the interface bond strength between concrete and the repair material. This study uses explainable deep learning techniques to evaluate the ultimate strength capacity (Us) of U-shaped normal concrete (NC) strengthened with polyurethane grouting (PUG) materials. Machine learning algorithms (ML) such as Long Short-Term Memory (LSTM), Random Forest (RF), and Wide Neural Network (WNN) models were developed to estimate Us by considering five input parameters: the initial crack strength (Cs), thickness of the grouting materials (T), mid-span deflection (λ), and peak applied load (P). The results indicated that LSTM models, particularly LSTM-M2 and LSTM-M3, demonstrated superior predictive accuracy and consistency in both the calibration and verification phases, as evidenced by high Pearson’s correlation coefficients (PCC = 0.9156 for LSTM-M2) and Willmott indices (WI = 0.7713 for LSTM-M2), and low error metrics (MSE = 0.0017, RMSE = 0.0418). The SHAP (SHapley Additive exPlanations) analysis showed that the thickness of the grouting materials and maximum load were the most significant parameters affecting the ultimate capacity of the composite U-shaped specimen. The RF model showed moderate improvements, with RF-M3 performing better than RF-M1 and RF-M2. The WNN models displayed varied performance, with WNN-M2 performing poorly due to significant scatter and deviation. The findings highlight the potential of LSTM models for the accurate and reliable prediction of the ultimate strength of composite U-shaped specimens. Full article

The conceptual design phase in architecture requires both intuitive and iterative approaches, which traditional Building Information Modeling (BIM) workflows fail to support properly. BIM provides data-driven decision-making and project coordination but does not offer affective or experiential feedback capabilities. BIM and Virtual Reality (VR) integration offers a promising solution to improve user-focused spatial assessments during initial design phases. The research follows three distinct phases, including a Systematic Literature Review to identify BIM-based conceptual workflow limitations, semi-structured interviews with architects to understand practical challenges and expectations, and the development of a BIM-based framework combining immersive VR for affective and visuospatial evaluation. A testing phase of the proposed framework occurred in the pilot study. The current BIM workflows show significant deficiencies in their ability to support creative flexibility, user engagement, and experiential validation. The BIM-VR framework implemented in the pilot study showed improvements in spatial cognition, emotional engagement, and iterative design decision-making during the conceptual design phase. Early-stage architectural design evaluation becomes more effective through VR integration into BIM workflows because it provides real-time immersive user feedback. The proposed framework helps develop BIM tools that are more intuitive for humans while advancing user-informed design practices in the architecture, engineering, and construction industries. Full article

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

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

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