Search Results (635)

Lava caves commonly occur in basaltic ground and can compromise roadway stability when present beneath pavements; however, their long-term effects remain insufficiently characterized. This study quantitatively evaluates how lava caves influence pavement behavior using numerical analyses in ABAQUS/CAE. The parameters examined include the presence/absence of a cave, cave width, cover depth, pavement thickness, and load range. Load–settlement curves under a uniformly distributed surface load show that narrower load ranges concentrate stresses and produce larger settlements, whereas wider load ranges disperse stresses and reduce deformation. Classification of deformation behavior using a rutting criterion indicates that plastic soil response dominates under most conditions. A Peak Load Reduction (PLR) index further demonstrates that structural resistance decreases markedly with shallow cover, larger cave width, and narrower load range. Overall, pavement stability above lava caves is governed primarily by cover depth, load range, and cave width, while the effect of pavement thickness is negligible. These findings suggest that, in basaltic terrains, design and maintenance should prioritize subsurface conditions and loading characteristics over pavement thickness. Full article

De-icing agents play a crucial role in winter road maintenance, yet their excessive application can result in pavement deterioration and environmental issues. Existing dosage guidelines lack comprehensive data on the dynamic response of de-icing agents under low-temperature conditions, particularly regarding stage-specific characteristics and multi-factor interactions. This research systematically evaluated the effectiveness of four de-icing agents (NaCl, CaCl₂, MgCl₂, CH₃COOK) within a temperature range of −5 °C to −25 °C, elucidating the two-phase ice-melting process (solid-phase followed by salt solution de-icing) with distinct kinetic mechanisms—a previously underexplored temporal pattern. The study quantified the differential impacts of particle size (small-particle CaCl₂ exhibiting 12% higher efficiency than sheet-like forms), dosage linear correlation, and negligible effects of ice layer thickness and road surface composition, which have not been systematically validated in prior studies. Temperature sensitivity was further refined: NaCl showed a 42.4% efficiency drop between −5 °C and −25 °C, while MgCl₂ maintained stable performance, supporting its potential as an environmentally sustainable alternative. This work provides a quantitative basis for dynamic dosage regulation by integrating stage characteristics and multi-factor optimization, addressing gaps in existing guidelines. Full article

This study investigates how short- and long-term aging affect the microwave self-healing of steel slag asphalt mixtures (SSAMs). Binder-level healing was tested using a dynamic shear rheometer (DSR), and mixture-level crack behavior was analyzed using beam bending tests (BBTs) and digital image correlation (DIC). Aging clearly reduced self-healing, with long-term aging causing the largest decline. Among the mixtures, OGFC-13 was most sensitive, while SMA-13 was least affected. Aging increased stiffness, reduced crack resistance, and shortened crack initiation time, leading to lower healing efficiency under microwave treatment. After heating, cracks propagated faster, indicating increased brittleness. These results quantify the impact of aging on performance and highlight the limitations of microwave repair, providing guidance for maintenance strategies and mixture design to improve long-term pavement performance. Full article

Optimizing pavement maintenance and rehabilitation (M&R) strategies is essential, especially in developing countries with limited budgets. This study presents an integrated framework combining a deterioration prediction model and a genetic algorithm (GA)-based optimization model to plan cost-effective M&R strategies for flexible pavements, including asphalt concrete (AC) and double bituminous surface treatment (DBST). The GA schedules multi-year interventions by accounting for varied deterioration rates and budget constraints to maximize pavement performance. The optimization process involves generating a population of candidate solutions representing a set of selected road sections for maintenance, followed by fitness evaluation and solution evolution. A mixed Markov hazard (MMH) model is used to model uncertainty in pavement deterioration, simulating condition transitions influenced by pavement bearing capacity, traffic load, and environmental factors. The MMH model employs an exponential hazard function and Bayesian inference via Markov Chain Monte Carlo (MCMC) to estimate deterioration rates and life expectancies. A case study on Cambodia’s road network evaluates six budget scenarios (USD 12–27 million) over a 10-year period, identifying the USD 18 million budget as the most effective. The framework enables road agencies to access maintenance strategies under various financial and performance conditions, supporting data-driven, sustainable infrastructure management and optimal fund allocation. Full article

Potholes threaten public safety and automated vehicles (AVs) safe navigation by increasing accident risks and maintenance costs. Traditional pavement inspection methods, which rely on human assessment, are inefficient for rapid pothole detection and reporting due to potholes’ random and sudden occurring. Advancements in Artificial Intelligence (AI) now enable automated pothole detection using image-based object recognition, providing innovative solutions to enhance road safety and assist agencies in prioritizing maintenance. This paper proposes a novel approach that evaluates the integration of 3 state-of-the-art AI models (YOLOv8n, YOLOv11n, and YOLOv12n) with an ADAS-like camera, GNSS receiver, and Robot Operating System (ROS) to detect potholes in uncontrolled real-life scenarios, including different weather/lighting conditions and different route types, and generate ready-to-use data in a real-time manner. Tested on real-world road data, the algorithm achieved an average precision of 84% and 84% in recall, demonstrating its effectiveness, stable, and high performance for real-life applications. The results highlight its potential to improve road safety, allow vehicles to detect potholes through ADAS, support infrastructure maintenance, and optimize resource allocation. Full article

Traditional road asset management relies on periodic, often inefficient, inspections. Digital Twins offer a paradigm shift towards proactive, data-driven maintenance by creating a real-time virtual replica of physical infrastructure. This paper proposes a comprehensive, formalized framework for a highway Digital Twin, structured into three integrated components: a Physical Space, which defines key performance indicators through mathematical state vectors; a Data Interconnection layer for real-time data processing; and a Virtual Space equipped with hybrid models. We provide a formal definition of these state vectors and a dynamic synchronization mechanism between the physical and virtual spaces. In this study, we focused on pavement condition assessment by using a data-driven component using accessible technology. This study show the synergy between the Digital Twin and deep learning, specifically by integrating advanced analytical models within the Virtual Space for intelligent pavement condition assessment. To validate this approach, a case study was conducted to classify road surface anomalies using low-cost Inertial Measurement Unit (IMU) data. We evaluated several machine learning classifiers and introduced a novel parallel Gated Recurrent Unit network. The results demonstrate that our proposed architecture achieved superior performance, with an accuracy of 89.5% and an F1-score of 0.875, significantly outperforming traditional methods. The findings validate the viability of the proposed Digital Twin framework and highlight its potential to achieve high-precision pavement monitoring using low-cost sensor data, a critical step towards intelligent road infrastructure management. Full article

This study evaluates the mechanical performance and sustainability potential of fiber-reinforced concrete incorporating mine tailings as the fine aggregate and waste tire wire as the reinforcing fiber. The concrete mixtures contained 0.2%, 0.4%, and 0.6% waste tire wire with the natural fine aggregate replaced entirely with Pb-Zn-Cu tailings. The mixtures were tested for porosity, water absorption, compressive strength, splitting tensile strength, flexural strength, toughness, fracture energy, and ductility to assess their mechanical performance and durability. The mine tailings improved the microstructure and reduced water absorption, particularly with tire wire. Using waste tire wire improved the compressive, tensile, and flexural performance; in particular, W-6 showed a 18.2% rise in compressive strength and a more than twofold increase in flexural strength relative to the control mix. The flexural toughness and fracture energy rose by up to 161%, while the ductility peaked at a fiber content of 0.2%. These gains were attributed to fiber crack-bridging and post-cracking energy absorption. The dual-waste system also reduced porosity, improved durability, and demonstrated strong potential for rigid pavement applications such as highways, industrial yards, and airport runways that require high fatigue resistance and a long service life. Beyond technical performance, this approach offers a sustainable solution that lowers maintenance, reduces life-cycle costs, and aligns with circular economy principles. Full article

This study proposes an optimization method that considers both section resurfacing and localized repairs of damaged points, based on the highly uneven distribution of pavement distress locations and under budget constraints. The model is formulated as an MILP (mixed-integer linear programming) model, where binary variables are used to simultaneously determine section resurfacing and localized repair actions. To overcome the excessive computation time required for large-scale road networks, a GA (genetic algorithm) is designed to perform heuristic searches. The model fully integrates information on distress locations, deduction values, and repair costs, and uses the maximization of the average PCI (pavement condition index) as the objective to ensure that decision-making focuses on tangible improvements in pavement service levels. Compared with the traditional point-by-point repair strategy, the proposed method can further increase the average PCI by approximately 3.5–4.0 points under medium- to high-budget conditions, demonstrating significant quantitative benefits. By simultaneously integrating section resurfacing and localized repair decisions, it saves about 10–15% more resources than individual repair methods while ensuring higher coverage of pavement distress. This method provides road maintenance agencies with a quantitative tool to flexibly allocate resurfacing and localized repair strategies under limited budgets. Full article

Preventive maintenance treatments are widely applied to asphalt pavements to mitigate deterioration and extend service life. This study evaluated four common technologies: a high-elasticity ultra-thin overlay, an Stone Mastic Asphalt (SMA)-10 thin overlay, micro-surfacing (MS-III), and a chip seal. Laboratory testing focused on skid resistance, surface texture, and low-temperature cracking resistance. Skid resistance was measured with a tire–pavement dynamic friction analyzer under controlled load and speed, while surface macrotexture was assessed using a laser scanner. Low-temperature cracking resistance was determined through three-point bending beam tests at −10 °C. The results showed that chip seal achieved the highest initial friction and texture depth, immediately enhancing skid resistance but exhibiting rapid texture loss and gradual friction decay. Micro-surfacing also demonstrated good initial skid resistance but experienced a sharp reduction of over 30% due to fine aggregate polishing. By contrast, the high-elastic ultra-thin overlay and SMA thin overlay provided more stable skid resistance, lower long-term friction loss, and excellent crack resistance. The polymer-modified ultra-thin overlay achieved the highest low-temperature bending strain ≈40% higher than untreated pavement, indicating superior crack resistance, followed by the SMA thin overlay. Micro-surfacing with a chip seal layer only slightly improved low-temperature performance. Overall, the high-elastic ultra-thin overlay proved to be the most balanced preventive maintenance option under heavy-load traffic and cold climate conditions, combining durable skid resistance with enhanced crack resistance. Full article

The durability of asphalt pavements is crucial for sustainable road infrastructures. This systematic review compares the Marshall and Superpave asphalt mix design protocols, with a particular focus on the integration of polymer-modified bitumen (PMB) and rejuvenators. Although the Marshall method remains widely used for its simplicity and cost-efficiency, its empirical basis limits its effectiveness to meet modern pavement performance demands. In contrast, the Superpave system offers improved resistance to rutting, longer fatigue life, and better mitigation of moisture damage. The review traces the evolution of asphalt mix design, identifies current challenges, and emphasizes the need for transitioning toward performance-based frameworks. Special attention is given to the incorporation of polymers such as Styrene–Butadiene–Styrene (SBS), Styrene–Butadiene–Rubber (SBR), and Polyethylene (PE), which significantly enhance the mechanical properties of asphalt mixtures. The role of rejuvenators in restoring aged binders and enabling pavement recycling is also examined. Finally, the manuscript provides strategic recommendations for adopting Superpave to enhance pavement durability and reduce lifecycle maintenance costs. Overall, this comprehensive review advances knowledge on asphalt mix design, fostering innovation and sustainability while promoting long-term resilience in road pavement infrastructures. Full article

Conventional asphalt pavement snow and ice removal methods suffer from issues such as time-consuming operations, high costs, and pollution from chemical de-icing agents. Commonly used thermally conductive asphalt concrete (TCAC) faces problems including limited filler diversity, high filler content, and elevated costs. To address these challenges, this study developed a thermally conductive asphalt concrete incorporating carbon fiber–silicon carbide composite fillers to provide a low-cost, energy-saving winter pavement snow melting solution and enhance eco-friendly de-icing performance. Finite element simulation software was employed to model its snow and ice melting performance, investigating the factors influencing this capability. Thermal conductivity was measured using the transient plane source (TPS) technique. The results show that with 0.3% carbon fiber, thermal conductivity reaches 1.43 W/(m·°C), 72.3% higher than ordinary asphalt concrete. Finite element simulations in finite element simulation software were used to model snow and ice melting, and strong agreement with field test data (correlation coefficients > 0.9) confirmed model reliability. Then, the finite element simulation software was used to study the effects of wind speed, temperature, laying power, and spacing on the snow and ice melting of TCAC. The simulation results show that the heating rate increases with TCAC thermal conductivity. Raising the power of the embedded carbon fiber heating cord reduces de-icing time but shows a threshold effect. In this study, asphalt pavement with high thermal conductivity was prepared using a low content of thermal conductive filler, providing a theoretical basis for sustainable pavement design, reducing energy use and environmental damage. TCAC technology promotes greener winter road maintenance, offering a low-impact alternative to chemical de-icing, and supports long-term infrastructure sustainability. Full article

Rehabilitation plans are based on pavement condition assessments, which are crucial to modern pavement management systems. However, some of the disadvantages of conventional approaches for road maintenance and repair include the time consumption, high costs, visual errors, seasonal limitations, and low accuracy. Continuous and efficient pavement monitoring is essential, necessitating reliable equipment that can function in a variety of weather and traffic conditions. UAVs offer a practical and eco-friendly alternative for tasks including road inspections, dam monitoring, and the production of 3D ground models and orthophotos. They are more affordable, accessible, and safe than traditional field surveys, and they reduce the environmental effects of pavement management by using less fuel and producing less greenhouse gas emissions. This study uses UAV technology in conjunction with ground control points (GCPs) to assess the kind and amount of damage in flexible pavements. Vertical photogrammetric mapping was utilized to produce 3D road models, which were then processed and analyzed using Agisoft Photoscan (Metashape Professional (64 bit)) software. The sorts of fractures, patch areas, and rut depths on pavement surfaces may be accurately identified and measured thanks to this technique. When compared to field exams, the findings demonstrated an outstanding accuracy with errors of around 3.54 mm in the rut depth, 4.44 cm² for patch and pothole areas, and a 96% accuracy rate in identifying cracked locations and crack varieties. This study demonstrates how adding GCPs may enhance the UAV image accuracy, particularly in challenging weather and traffic conditions, and promote sustainable pavement management strategies by lowering carbon emissions and resource consumption. Full article

Asphalt crack segmentation is essential for preventive maintenance but is sensitive to noise, viewpoint, and illumination. This study evaluates a minimally invasive strategy that augments standard RGB input with an auxiliary fourth channel—a crack-probability map generated by a multi-scale ensemble of classifiers—and injects it into segmentation backbones. Field imagery from unmanned aerial vehicles and action cameras was used to train and compare U-Net, ENet, HRNet, and DeepLabV3+ under unified settings; the probability map was produced by an ensemble of lightweight convolutional neural networks (CNNs). Across models, the four-channel configuration improved performance over three-channel baselines; for DeepLabV3+, the Intersection over Union (IoU) increased by 6.41%. Transformer-based classifiers, despite strong accuracy, proved less effective and slower than lightweight CNNs for probability-map generation; the final ensemble processed images in approximately 0.63 s each. Integrating ensemble-derived probability maps yielded consistent gains, with the best four-channel CNNs surpassing YOLO11x-seg and Transformer baselines while remaining practical. This study presents a systematic evaluation showing that probability maps from classifier ensembles can serve as an auxiliary channel to improve segmentation of asphalt pavement cracks, providing a novel modular complement or alternative to attention mechanisms. The findings demonstrate a practical and effective strategy for enhancing automated pavement monitoring. Full article

Accurate pavement crack detection under adverse weather conditions is essential for road safety and effective pavement maintenance. However, factors such as reduced visibility, background noise, and irregular crack morphology make this task particularly challenging in real-world environments. To address these challenges, we propose CrackNet-Weather, which is a robust and efficient detection method that systematically incorporates three key modules: a Haar Wavelet Downsampling Block (HWDB) for enhanced frequency information preservation, a Strip Pooling Bottleneck Block (SPBB) for multi-scale and context-aware feature fusion, and a Dynamic Sampling Upsampling Block (DSUB) for content-adaptive spatial feature reconstruction. Extensive experiments conducted on a challenging dataset containing both rainy and snowy weather demonstrate that CrackNet-Weather significantly outperforms mainstream baseline models, achieving notable improvements in mean Average Precision, especially for low-contrast, fine, and irregular cracks. Furthermore, our method maintains a favorable balance between detection accuracy and computational complexity, making it well suited for practical road inspection and large-scale deployment. These results confirm the effectiveness and practicality of CrackNet-Weather in addressing the challenges of real-world pavement crack detection under adverse weather conditions. Full article

Pavement damage classification is crucial for road maintenance and driving safety. However, restricted to the varying scales, irregular shapes, small area ratios, and frequent overlap with background noise, traditional methods struggle to achieve accurate recognition. To address these challenges, a novel pavement damage classification model is designed based on the MambaOut named Multi-scale Damage Enhancement MambaOut (MDEM). The model incorporates two key modules to improve damage classification performance. The Multi-scale Dynamic Feature Fusion Block (MDFF) adaptively integrates multi-scale information to enhance feature extraction, effectively distinguishing visually similar cracks at different scales. The Damage Detail Enhancement Block (DDE) emphasizes fine structural details while suppressing background interference, thereby improving the representation of small-scale damage regions. Experiments were conducted on multiple datasets, including CQU-BPMDD, CQU-BPDD, and Crack500-PDD. On the CQU-BPMDD dataset, MDEM outperformed the baseline model with improvements of 2.01% in accuracy, 2.64% in precision, 2.7% in F1-score, and 4.2% in AUC. The extensive experimental results demonstrate that MDEM significantly surpasses MambaOut and other comparable methods in pavement damage classification tasks. It effectively addresses challenges such as varying scales, irregular shapes, small damage areas, and background noise, enhancing inspection accuracy in real-world road maintenance. Full article