Search Results (35,399)

This study presents a systematic review of Explainable Artificial Intelligence (XAI) applications in Transportation Infrastructure Management (TIM), focusing on predictive maintenance of safety-critical assets such as railways and bridges. A predefined review protocol was implemented, and peer-reviewed literature was systematically retrieved from Web of Science and Scopus covering the period 2015 to March 2025. Using structured Boolean search logic and clearly defined inclusion and exclusion criteria—requiring explicit integration of explainability within AI-driven infrastructure maintenance—450 records were initially identified, screened in multiple stages, and refined to 163 eligible studies for detailed analysis. Through structured data extraction and thematic synthesis, the review develops a taxonomy of model-specific, model-agnostic, hybrid, and human-centered XAI approaches while identifying recurring challenges including heterogeneous multi-modal data environments, lack of standardized interpretability metrics, computational constraints in real-time deployment, limited robustness validation under field conditions, and unresolved performance–interpretability trade-offs. The findings demonstrate systematic growth in XAI-driven predictive maintenance research and highlight the need for domain-specific benchmarks, hybrid interpretable architectures, digital twin-assisted validation, and edge-enabled explainable systems to enable scalable, transparent, and regulation-ready infrastructure management aligned with Industry 5.0. Full article

Stone Matrix Asphalt (SMA) has emerged as a premier high-performance paving solution for critical infrastructure applications. Its distinctive skeleton structure, composed of coarse aggregates bound by a fiber-stabilized bituminous mastic, delivers exceptional mechanical performance, including superior resistance to rutting (≤3 mm after 10⁶ load cycles) and fatigue cracking (>500,000 cycles to failure). While proven in demanding service environments, research has increasingly focused on enhancing the sustainability of SMA through key innovations: (1) the incorporation of recycled materials, such as 30–40% Reclaimed Asphalt Pavement (RAP) and 0.3–0.5% waste tire textile fibers (WTTF); (2) the development of bio-based binders derived from renewable sources; and (3) the adoption of Warm-Mix Asphalt (WMA) technologies that reduce production temperatures by 20–30 °C. These advancements yield significant environmental benefits, including approximately 25% lower CO₂ emissions and 15–20% reduced energy consumption compared to conventional SMA production. It is important to distinguish between these quantitatively demonstrated benefits, primarily from Life Cycle Assessment (LCA) studies of technologies like WMA and RAP, and the more qualitative sustainability claims associated with emerging materials like nanomaterials or novel bio-additives, which often lack comprehensive lifecycle inventories. Nevertheless, challenges persist, notably moisture susceptibility (manifesting as a 10–15% strength reduction after saturation) and uncertainties regarding the long-term performance of modified mixes. This review consequently identifies critical research priorities: optimizing mix designs with locally available materials to minimize transport emissions, employing nano-scale modifiers to enhance moisture resistance, and developing standardized lifecycle assessment protocols. Addressing these challenges is paramount to establishing SMA as a model sustainable pavement technology that robustly meets both structural performance benchmarks and ecological sustainability goals. Full article

In Amazonian cities, river landscapes may function as key spaces of environmental, social, and cultural convergence, particularly in areas with significant human activity. This research proposes the design of an ecological corridor as a multifunctional public space that strengthens the relationship between the city of Iquitos (Loreto, Peru) and its river environment, promoting user comfort, sustainability, and the revaluation of water resources. The methodology is based on analyses of local flora and fauna, climatic conditions, and the application of passive architectural strategies, supported by digital tools such as AutoCAD 2024, Google maps 2025, OpenStreetMap, Photoshop 2024, SketchUp 2024, and Snazzy Map. The proposal integrates renewable energy through the installation of 55 photovoltaic-powered lampposts mainly distributed along the road and pedestrian infrastructure of the corridor, responsible water management via rainwater harvesting systems, and the use of local eco-friendly materials, including capirona wood for structural elements, bolaina wood for furniture and finishes, and bamboo for shading structures. Additionally, 81.61% of the total area is allocated to green spaces with native flora, complemented by an Amazonian plant nursery. Although similar integrative riverfront regeneration projects have been implemented in several international cities, their application in Amazonian urban contexts remains limited, highlighting the relevance of this proposal. In conclusion, the project aligns with the Sustainable Development Goals and contributes to contemporary discussions on public space planning in tropical contexts, proposing an ecological regeneration model adaptable to other Amazonian cities. Full article

High-speed railway signaling equipment constitutes safety-critical infrastructure, wherein hardware failures may directly compromise operational safety. During the hardware prototyping and verification stage, structural testing is essential to detect latent faults in digital logic circuits and to ensure compliance with stringent safety integrity requirements. However, conventional test generation methods often suffer from long generation times and excessive test vector volume. To address these challenges, this study proposes a whale optimization-based dynamic compression Automatic Test-Pattern Generation (ATPG) algorithm. The proposed method integrates a discrete whale optimization algorithm (WOA) with a deterministic PODEM framework to dynamically compress generated test vectors. Additionally, a multi-path-sensitized PODEM enhanced with desensitization techniques is introduced to reduce backtracking and improve search efficiency. The proposed algorithm has been applied to the computer interlocking golden model netlist for testing purposes, achieving an impressive fault coverage rate of 100%. Test results from the ISCAS-85 standard circuit indicate that our approach significantly reduces both the length of the vector set and the time required for test generation when compared to traditional PODEMs without vector compression and pseudo-random combined PODEM vector generation methods. This advancement effectively enhances overall vector generation efficiency while maintaining comprehensive fault coverage. Full article

The transition to electric mobility requires the coordinated evolution of vehicles, charging infrastructure, power systems, and intelligent digital platforms. This study examines the role of Industry 4.0 technologies in enabling large-scale electric vehicle (EV) adoption and effective EV grid integration and synthesizes the existing evidence into a coherent analytical framework to support planning and policy decision-making. A systematic review of 27 peer-reviewed studies published between 2018 and 2025 was conducted in accordance with PRISMA 2020 guidelines, capturing the acceleration of electromobility following the consolidation of Industry 4.0 technologies and the emergence of large-scale policy commitments worldwide. The analysis covers six technology families, including the Internet of Things, big data and analytics, artificial intelligence and machine learning, blockchain, digital twins, and extended reality, and examines their applications in smart charging, grid vehicle coordination, fleet optimization, and vehicle-to-grid services. The findings show that analytics and artificial intelligence consistently enhance operational reliability and efficiency, while digital twins are increasingly applied to infrastructure siting, grid impact assessment, and scenario analysis. Building on these results, the study proposes a three-layer analytical framework composed of physical, digital, and decision layers, together with a functional EV grid generation integration model that links technology readiness to system-level deployment. In addition, a transition timeline for the 2025–2040 period and a concise set of key performance indicators are introduced to support evaluation and comparison. Policy implications for Ecuador and Latin America emphasize interoperability, data governance, realistic cost assessment, and a phased approach to vehicle-to-grid deployment. Full article

Safe operation of aging dam infrastructure requires regular updates to design flood estimates and spillway operating rules. This paper presents a semi-automated methodology for updating design floods for return periods of 10 to 10,000 years, utilizing a suite of open-source software tools. The framework was applied to the El Caracol, Infiernillo, and Villita dams within the Balsas River hydroelectric system in Mexico. The methodology is based on the Engineering Institute of National Autonomous University of Mexico (IIUNAM) procedure, incorporating frequency analysis of daily mean flows and the generation of design hydrographs using the AX and IIHIDRODIS programs. Flood routing was subsequently performed under various operational policies using the Trate, HEC-HMS, and Traven programs to develop safe and effective strategies. Key recommendations include: for El Caracol dam, a staggered discharge policy for floods exceeding a 50-year return period (Tr); for Infiernillo dam, limiting discharges to 6000 m³/s during frequent floods (Tr < 100 years) and up to 12,000 m³/s for extreme events; and for La Villita dam, operating at 6000 m³/s, potentially increasing to 10,000 m³/s under extreme conditions while respecting downstream channel capacity. The proposed sequential automation markedly reduces processing times, in particular during the sequence involved in constructing a design flood. Previously, this task required approximately one or two days; it can now be completed in just a few minutes, demonstrating a significant advancement in the state of knowledge for studies on dam safety reassessment. Full article

The concrete cracking problem can seriously affect the durability and safety of civil structures. Accurately and quickly measuring the width of concrete cracks can help control defect development in a timely manner. Current research mainly relies on pixel detection of two-dimensional images, which lacks real three-dimensional information about crack lesions. Detection results are also obviously affected by various factors, such as shooting distance and posture, resulting in poor accuracy. Therefore, this paper presents an engineering-integrated solution that combines U-Net-based crack segmentation with binocular vision 3D reconstruction. The focus is placed on the practical deployment of the integrated pipeline, the optimization of key parameters under real inspection conditions, and the experimental validation of measurement accuracy on actual concrete cracks. Firstly, the U-Net deep learning algorithm is used to automatically identify and segment the concrete crack region; then, a binocular vision-based 3D reconstruction pipeline is adopted, and a parallax rejection algorithm based on a “double-threshold” decision is proposed to improve the fidelity of crack disparity maps, and the effect of the filter window size on the concrete crack region is analyzed; finally, an intelligent measurement method based on the 3D reconstruction model is proposed, and the measurement results of concrete crack width can be calculated directly from the 3D reconstruction model. The results show that (1) the model can identify the characteristics of the crack, and the detection effect at 4:00 p.m. is the best, because at this time the light is more uniform with less shadow and moderate contrast between the crack and its background; (2) the reconstruction of the 3D point cloud model of the concrete crack with a filtering window of size 9 × 9 is the best; (3) the maximum error between the calculated and measured values of crack width is 0.31mm, the minimum error is 0.07mm, and the average error is 0.15 mm, which indicates that the measurement accuracy reaches the sub-millimetre level and verifies the validity of the proposed method in this paper. Full article

Heavy-haul railways are important for bulk freight transport, and improving their transport capacity is essential for railway operators to enhance operational efficiency. This study develops an integer linear programming model for train formation planning that maximizes transport capacity, incorporating key practical constraints such as section headway, station capacity, and locomotive matching. This study makes two main contributions: (1) explicit formulation of transport-capacity maximization as the primary objective; and (2) incorporation of specific train formation rules through linear resource-flow coefficients that characterize the combination and decomposition operations. The model is applied to the Shuozhou–Huanghua Railway in a case study. Experimental results show that the optimized train formation plan increases total freight volume from 2810.4 thousand tons to 3080.0 thousand tons, representing a capacity improvement of approximately 9.6%. This result is achieved by adjusting the mix of train tonnage levels, increasing combination operations for medium-capacity trains, and reallocating locomotive types in accordance with traction requirements. The study demonstrates that a capacity-oriented optimization framework can effectively support train-formation plan decisions under practical operational constraints, providing railway operators with a systematic tool to enhance line utilization without expanding infrastructure. Full article

The integration of unmanned aerial vehicles (UAVs) into vehicular ad hoc networks (VANETs) has emerged as a promising solution to overcome the limited coverage of conventional roadside unit (RSU)-based infrastructures. However, UAVs operate in open environments and cannot be fully trusted, while the rapid advancement of quantum computing threatens the long-term security of classical public-key cryptographic systems. As a result, many existing UAV-based VANET authentication schemes face fundamental limitations in future deployments. Most existing schemes either lack post-quantum security or incur excessive computational and communication overhead, making them unsuitable for real-time and high-mobility vehicular environments. In addition, the common assumptions of trusted UAVs do not align with realistic threat models. To address these issues, this paper proposes a lightweight post-quantum authentication and key exchange protocol based on the module learning with errors (MLWE) problem and physically unclonable functions (PUFs). The proposed scheme treats UAVs as untrusted relay nodes and excludes them from session key generation. Its security is evaluated using informal analysis, the real-or-random (RoR) model, BAN logic, and AVISPA, while performance evaluation indicates improved efficiency compared to existing schemes. Full article

While self-healing concrete shows promise for infrastructure repair, its effectiveness is significantly compromised in low-temperature environments because of slowed reaction kinetics and the embrittlement of capsule shells. To address this limitation, novel composite microcapsules featuring an ethyl cellulose shell and a dual-core comprising expansive cement and epoxy resin were developed. These microcapsules were fabricated using a physical spheronization-coating method and subsequently incorporated into cement mortar. Response surface methodology was employed to identify the optimal system, which balances self-healing performance with the retention of mechanical properties: a microcapsule content of 3% (by mass of cement) and a particle size range of 1.4 to 1.7 mm. Under conditions of −20 °C, the optimal formulation achieved a crack surface healing ratio of up to 44.1% and a compressive strength recovery of up to 6.0%. Microstructural and spectroscopic analyses (SEM-EDS, XRD) revealed a synergistic healing mechanism. This mechanism involves the formation of calcium carbonate, C–S–H gel, and anorthite, all cohesively bonded within a polymerized epoxy network. This work establishes a functional material strategy for enabling autonomous crack repair in concrete structures subjected to cold climates. In such environments, even marginal strength recovery, when coupled with effective crack sealing, can significantly enhance structural durability. Full article

The assessment of flood defense structures is essential for community resilience and disaster prevention. Within these structures, the potential for erosion and piping mechanisms poses critical risks, often leading to severe infrastructure damage. Breach initiation and growth are the main causes of dam and levee failure, which is directly affected by the phreatic line. This study introduces a natural element method (NEM) formulation with Sibson interpolation specifically tailored to directly estimate the phreatic line in homogeneous earthen embankments, avoiding conventional mesh generation and reducing preprocessing effort. The main innovation is the combination of a mesh-free NEM scheme with an iterative free-surface update dedicated to phreatic line tracking, rather than full embankment flow field simulation. Comparative analyses and validation against existing data emphasize the method’s strength. Validation against piezometric data from a railway embankment in Cumbria (UK) and the IJkdijk full-scale test levee (Netherlands) shows average relative errors below 2% and maximum errors under 10%, demonstrating that the proposed NEM approach can reproduce observed phreatic levels with high accuracy using relatively few nodes. These results indicate that the method provides an accurate and practically attractive tool for phreatic line assessment in flood defense structures, suitable for integration into levee and embankment safety evaluations. Full article

In disaster scenarios where communication infrastructure is damaged, Unmanned Aerial Vehicle (UAV)-assisted wireless networks can provide temporary connectivity and hence the indispensable mobile edge computing functionality. However, limited resources on UAVs require prioritization of critical data in such scenarios. This research addresses reliable transmission and task offloading by modeling user tasks as layered compositions, where the base layer is essential and enhancement layers are optional. TDMA-based prioritization is employed to ensure reliable decoding of high-priority layers of the computational tasks (i.e., intra-user priority) along with inter-user priority needed for urgent users like rescue teams. Under these reliability constraints, this work formulates a joint communication–computation optimization problem to allocate transmission power and UAV CPU cycles efficiently in order to minimize total weighted offloading latency. The original problem is non-convex; thus, we leverage epigraph and perspective functions to recast the problem into a convex one. We also derive analytically, using the KKT conditions, the optimal water-filling-like solutions for the reformulated problem. The numerical results show that, at a signal-to-noise ratio of 5 dB, the proposed algorithm achieves relative latency reductions vs. the baseline algorithms (39.99% reduction vs. Equal Allocation, 49.99% reduction vs. Enhancement First, and 69.99% reduction vs. No Priority), which reflect considerable latency reduction with priority-aware offloading. Full article

This study investigates the synergistic use of Fischer–Tropsch wax (FTW) and recycled rubber powder (RP) as dual modifiers in stone mastic asphalt (SMA11) to improve its mechanical and functional performance. Rheological analysis demonstrated that an FTW content of 4% achieves the optimal balance of high-temperature rutting resistance, aging resistance, and workability, with a binder viscosity of 1.6 Pa·s at 135 °C. When incorporated into SMA11 mixtures at 15%, RP yielded the best overall mechanical performance, including a reduction in rut depth to 1.22 mm and a 25% decrease in wheel tracking slope (WTS). The 15% RP mixtures also exhibited superior long-term skid resistance (μ_m = 0.329 after 180,000 polishing cycles, corresponding to a 13% reduction in braking distance) and enhanced thermal cracking resistance (failure temperature improved by 8.0 °C to −32.7 °C). An RP content of 5% maximized moisture resistance (ITSR = 100%), while 10% RP produced the highest mid-frequency sound absorption coefficient (α = 0.050). The hybrid modification system enables a 20 °C reduction in production temperature, consistent with published data on wax-based warm-mix technologies, and is associated with reduced energy consumption and lower emissions. The approach simultaneously supports sustainable pavement design through the high-value reuse of waste tire rubber. Full article

This study investigates the impact of urban meteorological factors on road crash severity in Dhaka, Bangladesh. Using police crash data, and meteorological data from NASA POWER database for years 2011–2022, a generalized ordered logit model was used to analyze crash severity, and interpreted using odds ratio, log odds ratio, predicted probabilities and marginal effects. The results show that land surface temperature (LST), relative humidity, precipitation, surface pressure, and wind speed have significant association with crash severity. Relative humidity, surface pressure and LST exhibited positive relation with higher severity levels of crashes, whereas precipitation had a negative relation. We recommend three actions to lessen the severity of crashes during inclement weather based on the findings: (i) weather-responsive transport safety policies, which incorporate real-time weather data into intelligent transport systems; (ii) law enforcement-oriented policy implications, which include using automated speed cameras and red-light violation cameras to improve compliance consistency and updating driver training courses to include modules on risk perception across various environmental conditions; and (iii) infrastructure and vehicle-related policy implications, which include designing road geometries and surface conditions to prevent the effects of adverse weather conditions and utilizing safety equipment, such as electronic stability control and anti-lock braking systems. Full article