Search Results (171)

To address the growing demand for sustainable road infrastructure development and resolve technical bottlenecks in reclaimed asphalt pavement (RAP) recycling, this study optimized the performance of recycled asphalt mixtures (RAMs) and validated their engineering applicability for field construction. RAM specimens were prepared using 5-year and 10-year aged RAP from Ningxia, with a constant RAP content of 30%. Laboratory tests including high-temperature rutting, moisture susceptibility, low-temperature cracking, dynamic modulus, and four-point bending fatigue were performed to determine the optimal mix proportion. Fourier Transform Infrared Spectroscopy (FTIR) and Thin-Layer Chromatography-Flame Ionization Detection (TLC-FID) were employed to reveal the regeneration mechanism of waste engine oil (WEO). Results showed that WEO modified the functional groups and four fractions of asphalt, optimizing its colloidal structure, while excessive WEO compromised high-temperature stability. The optimal WEO contents were 4% for RAP (5Y) and 8% for RAP (10Y), which significantly enhanced the overall performance of RAM to adapt to Ningxia’s climate. This study provides technical support for sustainable road infrastructure in arid and semi-arid regions. Full article

To improve the accuracy and efficiency of road crack detection, this paper proposes an enhanced model based on You Only Look Once version 8 (YOLOv8). The Cross Stage Partial DarkNet (CSPDarkNet) backbone network is replaced with TransXNet, which integrates a Transformer-based self-attention mechanism with convolutional operations to better capture local features. By embedding the attention mechanism into its core module, namely the dual dynamic token mixer (D-Mixer), the TransXNet architecture is further optimized, thereby enabling coordinated attention-based feature selection and enhancement across both global and local dimensions. In addition, the Gaussian Error Linear Unit (GELU) is employed to enhance nonlinear representation capability and training stability. Experiments were conducted on the public Road Damage Dataset 2022 (RDD2022), which contains approximately 47,000 road images. The results demonstrate that, compared with the baseline model, the precision, recall, and mean Average Precision (mAP) of the proposed method are improved by 9.6, 11.7, and 8.2 percentage points, respectively; moreover, the detection accuracy and computational efficiency also outperform those of other methods. Full article

Intelligent road distress detection technology has emerged as an important research topic in the field of highway maintenance. However, the accuracy and practicality of pavement distress detection are constrained by multiple factors, primarily including the irregular shapes of distress, the tendency for fine cracks to be overlooked, and the high parameter count of detection models that makes deployment difficult. Therefore, this study proposes a lightweight road distress detection model based on an improved RT-DETR architecture—LRD-DETR. First, this work integrates the C2f-LFEM module with the ADown adaptive down-sampling strategy into the backbone network, significantly reducing the number of model parameters and computational load while effectively enhancing the representation capacity of multi-scale pavement distress features. Second, a frequency-domain spatial attention is embedded in the S4 feature layer, where synergistic integration of frequency-domain filtering and spatial attention enables detail enhancement of distress edges and contours, automatically focuses on the distress regions, and suppresses background interference. The polarity-aware linear attention is incorporated into the S5 feature layer, by explicitly modeling polarity interactions, it effectively captures textural discrepancies between damaged regions and the intact road surface, and a learnable power function dynamically rescales attention weights to strengthen distress-specific feature responses. Finally, a cross-scale spatial feature fusion module (CSF²M) is developed to reconstruct and fuse multi-level spatial featurez, thereby improving detection robustness for pavement distresses with diverse morphologies under complex background conditions. Quantitative experiments indicate that, in contrast with the baseline RT-DETR, the presented framework improves the F1-score by 7.1% and mAP@50 by 9.0%, while reducing computational complexity and parameter quantity by 43.8% and 38.0%, respectively. These advantages enable LRD-DETR to be suitably deployed on resource-limited embedded platforms for real-time road distress detection. Full article

Under continuous erosion of dynamic water pressure generated by vehicle–water–pavement coupling interaction, asphalt mixture will gradually deteriorate and severe moisture damage finally emerges. The fine aggregate mixture (FAM) component is notably eroded and stripped, while the aggregate component even cracks sometimes. Sufficient attention has not been paid to these critical phenomena. This study employed the 3D laser scanning technique to detect changes in surface roughness of the asphalt mixture before and after it was eroded by dynamic water pressure. The degree of erosion of the asphalt mixture, FAM component, and aggregate component were thereby evaluated. The influences of experimental parameters such as water temperature and pore water pressure magnitude, as well as variable parameters including lithology and asphalt type, were also taken into account. By integrating the detection of physical and mechanical properties evolution of aggregates, the mechanism of moisture damage was comprehensively illustrated from the perspectives of both components of FAM and aggregate. The findings revealed that the 3D laser scanning technique could clearly detect and quantitatively assess the morphological changes on the asphalt mixture surface after been eroded in dynamic water pressure. Both types of asphalt mixtures exhibited varying degrees of erosion and wear, and obvious increases in surface unevenness were observed in each case. Variations in either temperature or pore water pressure magnitude showed limited influence on moisture damage in basalt-based asphalt mixture. In contrast, moisture damage sustained by limestone-based asphalt mixture was notably sensitive to temperature changes but remained largely insensitive to fluctuations in pore water pressure magnitude. The increase in surface roughness of asphalt mixture was primarily attributed to the scouring action of dynamic water pressure, which removed the FAM component surrounding coarse aggregate particles. Degradation in coarse aggregate particles would lead to the deterioration of the entire asphalt mixture. The compatibility between the stripping rate of FAM component and the deterioration rate of coarse aggregate governed the macroscopic manifestation of overall moisture damage in the asphalt mixture. Full article

Low-grade highway maintenance faces the challenge of high demand yet limited resources. Accurately identifying road damage is a key task to improve maintenance efficiency, which is crucial for addressing this demand–resource contradiction. To address this issue, YOLOv5s was selected as the foundational model due to its superior balance of detection accuracy, speed, and computational efficiency compared to other YOLO variants. Comprehensive optimizations were then implemented to further enhance its performance, including the development of a Global Context Squeeze (GS) module, a modified loss function, optimized Non-Maximum Suppression (NMS), and targeted image preprocessing strategies. The GS module is designed to effectively integrate contextual information, expand the receptive field, capture long-range dependencies, and strengthen feature extraction capabilities. A suburban road section in Shanghai with typical pavement damage was selected as the experimental site, where 8515 images were collected for model training and testing. Experiments demonstrated that the optimized YOLOv5s-G model achieved a mean average precision (mAP) of 90.7% for crack detection, a relative improvement of 18.6% over the original YOLOv5s. Furthermore, it outperformed models employing conventional optimization strategies, such as those with added small object detection layers or standard attention mechanisms. The superior performance of the YOLOv5s-G model significantly enhances pavement crack detection accuracy, offering technical support to improve low-grade highway maintenance efficiency and alleviate pressures from resource limitations. Full article

The application of three-dimensional ground-penetrating radar (3D GPR) for intelligent pavement defect analysis is often constrained by the limited availability of labeled samples. To address this challenge, this study employed Ground Penetrating Radar Maxwell (GprMax) to simulate typical pavement defects, including cracks, loose materials, and interlayer debonding. A Cycle-Consistent Generative Adversarial Network (Cycle-GAN) was then introduced to perform style transfer on the simulated images, thereby reducing the domain gap between simulated and real radar images. Furthermore, four You Only Look Once (YOLO) models—YOLO version 5, YOLOX, YOLO version 7, and YOLO version 8—were systematically compared using real datasets to identify the best-performing model, which was subsequently used to evaluate the effect of different proportions of synthetic data on detection performance. The results demonstrated that the moderate inclusion of synthetic data improved the recognition accuracy of loose defects (from 76.7% to 78.9%), whereas its impact on crack and debonding detection was negative. Moreover, excessive reliance on synthetic data led to overfitting, thereby reducing the model’s generalization capability. Among the four models, YOLOv7 achieved the best overall performance, with a mean Average Precision (mAP) of 83.4% and a crack detection rate of 88.2%. This study thus provides a feasible technical pathway and model selection reference for automated GPR-based pavement defect identification, offering practical value for efficient and accurate road maintenance inspections. Full article

The intelligent detection of asphalt pavement cracks has become increasingly important for ensuring service performance of road infrastructure. Traditional manual detection has significant safety hazards and insufficient accuracy. Furthermore, existing deep learning models still face challenges, including missed detection, false alarms, and poor performance in small target detection under complex conditions. This investigation adopts unmanned aerial vehicles (UAVs) to acquire pavement distress information and develops an intelligent detection approach for asphalt pavement crack based on improved YOLOv8s. First, the Spatial Pyramid Pooling Fast (SPPF) module is replaced with the Spatial Pyramid Pooling Fast with Cross Stage Partial Connections (SPPFCSPC) module in the backbone network to enhance the multi-scale feature fusion capability. Secondly, the Convolutional Block Attention Module (CBAM) module is introduced to the neck network to optimize the feature weights in both channel and spatial attention. Meanwhile, the Efficient Intersection over Union (EIoU) loss is adopted to improve accuracy. Finally, the Crack_Dataset is established, and the ablation experiments are conducted to verify the reliability of the detection model. The research indicates that the improved model achieves Precision, Recall, and mAP@0.5 of 83.9%, 79.6%, and 83.9%, respectively, representing increases of 1.5%, 1.3%, and 1.4%, compared with the baseline model. In comparison with mainstream object detection algorithms such as YOLOv5s and YOLOv8s, the proposed method attains an F1-score, mAP@0.5, and mAP@[0.5–0.95] of 0.82, 83.9%, and 46.6%, respectively, demonstrating a performance improvement. Based on the improved detection model, a pavement crack detection system was designed and implemented using PyQt5. This system supports image, video, and real-time camera input and detection. Full article

Accurate detection of road surface defects and their integration into geospatial representations are key requirements for scalable UAV-based inspection and maintenance systems.This work presents a lightweight processing pipeline that converts image-based pavement defect segmentations into compact geospatial vector representations suitable for integration with GIS-driven inspection workflows. In addition, we introduce and publicly release a UAV-based road defect dataset with pixel-level annotations, specifically designed for crack-like pavement damage. A deep convolutional neural network is trained to perform semantic segmentation of pavement defects using images derived from the publicly available RDD2022 dataset. Segmentation performance is evaluated across a range of probability thresholds using standard pixel-wise metrics, and a validation-selected operating point is used to generate binary defect masks. These masks are subsequently processed to identify individual defect instances and extract vector polygons that preserve the underlying geometry of crack-like structures. For illustrative geospatial integration, predicted defects are projected into geographic coordinates and exported in standard GIS formats. By transforming dense segmentation outputs into compact georeferenced polygons, the proposed framework bridges deep learning-based perception and GIS-based infrastructure assessment, enabling instance-level geometric analysis and providing a practical representation for UAV-based road inspection scenarios. Full article

Pavement maintenance programs rely on timely and accurate crack assessment to preserve roadway quality and reduce long-term rehabilitation costs. Manual inspection remains the prevailing practice, yet it is slow, subjective, and exposes crews to safety risks. Automating crack detection under real-world roadway conditions remains challenging due to inconsistent lighting, shadows, stains, and surface textures that obscure distress features. This study examines the applicability of an integrated, vehicle-mounted framework for automated pavement crack segmentation and width-based severity estimation under practical roadway operating conditions. Data were collected from a moving vehicle using a custom camera–GPS system operating under diverse conditions, capturing the variability encountered in practical surveys. The proposed approach employs a state-of-the-art segmentation model and a calibrated width estimation tool that converts pixel-level crack measurements into physical units using a position-dependent regression model. The key contribution of this work is a unified segmentation and severity evaluation pipeline supported by a novel pixel-to-inch calibration surface and validated using images acquired during normal driving operations and manual field crack measurements. By combining advanced computer vision techniques with practical field-oriented data collection, the proposed system provides a deployable solution for roadway crack assessment, enabling safer, faster, and more scalable network-level pavement monitoring. Full article

The identification and monitoring of pavement pathologies are critical for maintaining road infrastructure and ensuring transportation safety. As traditional inspection methods are often time-consuming, labor-intensive, and prone to human error, in recent years, Unmanned Aerial Vehicles (UAVs) have emerged as a promising tool for pavement condition assessment due to their mobility, efficiency, and ability to capture high-resolution imagery and multi-sensor data. This Systematic Literature Review aims to synthesize and evaluate existing research on the use of UAV for identifying pavement pathologies, such as cracks, potholes, rutting, and surface degradation. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology, publications were screened and selected across major academic databases such as Scopus and Web of Science. A total of 361 relevant articles published from 2020 to July 2025 were identified and analyzed using bibliometric overview. And a full-text synthesis and qualitative analysis was performed on a subset of 108 studies, which met the quality assessment criteria. The review categorizes the UAV systems, computer vision approaches, pathology types, and pavement materials examined in the studies. The findings indicate a growing trend in the use of UAV and computer vision techniques for pavement pathology detection, along with evolving preferences for UAV platforms, analytical approaches, and targeted pathology categories over time. This review highlights current gaps and outlines future research directions to advance UAV-based pavement pathology identification as a viable and reliable alternative to conventional inspection methods. Full article

The subgrade is a critical component of highway infrastructure that directly affects pavement performance and traffic safety. With the rapid expansion of highway networks and increasing heavy-truck traffic, latent subgrade distresses, such as insufficient base strength, uneven settlement, and base cracking, have become key factors limiting pavement serviceability. These distresses are often difficult to detect at early stages and may evolve into sudden structural failures if not properly identified. This study investigates the evolution mechanisms and spatial characteristics of representative subgrade distresses through an integrated framework combining FWD screening, GPR imaging, core sampling, and Abaqus-based finite element simulation. Field data were collected from the Changshen Expressway. Potential weak zones were first identified using FWD testing and further localized by GPR, while multilayer constitutive parameters were obtained from core sample analyses. The field-derived material parameters were then incorporated into an FE model to simulate pavement responses under loading and to interpret the underlying distress mechanisms. The proposed framework enables identification of dominant distress types, quantification of stiffness degradation, and clarification of deterioration pathways within the subgrade system. The results provide practical support for condition assessment, health monitoring, and maintenance decision-making in highway infrastructure. Full article

The rapid growth of urban areas has increased the need for more efficient methods of pavement inspection and maintenance. However, conventional techniques remain slow, labor-intensive, and limited in spatial coverage, and their performance is strongly affected by traffic, weather conditions, and operational constraints. In response to these challenges, it is essential to synthesize the technological advances that improve inspection efficiency, coverage, and data quality compared to traditional approaches. Herein, we present a systematic review of the state of the art on the use of unmanned aerial vehicles (UAVs) for monitoring and assessing pavement deterioration, highlighting as a key contribution the comparative integration of sensors (photogrammetry, LiDAR, and thermography) with recent automatic damage-detection algorithms. A structured review methodology was applied, including the search, selection, and critical analysis of specialized studies on UAV-based pavement evaluation. The results indicate that UAV photogrammetry can achieve sub-centimeter accuracy (<1 cm) in 3D reconstructions, LiDAR systems can improve deformation detection by up to 35%, and AI-based algorithms can increase crack-identification accuracy by 10% to 25% compared with manual methods. Finally, the synthesis shows that multi-sensor integration and digital twins offer strong potential to enhance predictive maintenance and support the transition towards smarter and more sustainable urban infrastructure management strategies. Full article

With the acceleration of urbanization, municipal landscape roads play a crucial role in urban public spaces. This study focuses on the distress detection and aging characteristics of asphalt pavements in municipal landscape roads. Firstly, a novel method is proposed based on the SpA-Former shadow removal network, which effectively addresses the interference caused by tree shadows and significantly improves the accuracy of automated distress identification. Distress detection results indicate that transverse cracks are the most common type of distress, primarily influenced by environmental factors such as asphalt material aging, temperature fluctuations, and freeze-thaw cycles—these factors induce asphalt embrittlement and a substantial decline in crack resistance. Subsequently, accelerated aging experiments were conducted to simulate the aging process of asphalt materials. It was found that as aging time extends, asphalt stiffness increases significantly; while this enhances deformation resistance, it also makes the material more prone to cracking under low-temperature conditions. Low-temperature crack resistance tests reveal that asphalt aged for more than six years exhibits a sharp deterioration in low-temperature crack resistance, showing distinct brittle characteristics. Furthermore, freeze-thaw cycle experiments demonstrate that the coupling effect of asphalt aging and freeze-thaw action significantly impairs its freeze-thaw resistance—particularly for asphalt aged over six years, which nearly loses its freeze-thaw resistance. In summary, the coupling effect of asphalt aging and environmental factors is the primary cause of pavement damage in municipal landscape roads. This study divides 2542 images into three mutually exclusive subsets: a training set of 2123 images, a validation set of 209 images, and a test set of 210 images. The research provides new theoretical references and technical support for the maintenance and management of landscape roads, especially demonstrating practical significance in distress detection and the analysis of material aging mechanisms. Full article

The prompt and precise detection of road damage is vital for effective infrastructure management, forming the foundation for intelligent transportation systems and cost-effective pavement maintenance. While current convolutional neural network (CNN)-based methodologies have made progress, they are fundamentally limited by treating damages as independent, isolated entities, thereby ignoring the intrinsic spatial symmetry and topological organization inherent in complex damage patterns like alligator cracking. This conceptual asymmetry in modeling leads to two major deficiencies: “context blindness,” which overlooks essential structural interrelations, and “temporal inconsistency” in video analysis, resulting in unstable, flickering predictions. To address this, we propose a Spatio-Temporal Graph Mamba You-Only-Look-Once (STG-Mamba-YOLO) network, a novel architecture that introduces a symmetry-informed, hierarchical reasoning process. Our approach explicitly models and integrates contextual dependencies across three levels to restore a holistic and consistent structural representation. First, at the pixel level, a Mamba state-space model within the YOLO backbone enhances the modeling of long-range spatial dependencies, capturing the elongated symmetry of linear cracks. Second, at the object level, an intra-frame damage Graph Network enables explicit reasoning over the topological symmetry among damage candidates, effectively reducing false positives by leveraging their relational structure. Third, at the sequence level, a Temporal Graph Mamba module tracks the evolution of this damage graph, enforcing temporal symmetry across frames to ensure stable, non-flickering results in video streams. Comprehensive evaluations on multiple public benchmarks demonstrate that our method outperforms existing state-of-the-art approaches. STG-Mamba-YOLO shows significant advantages in identifying intricate damage topologies while ensuring robust temporal stability, thereby validating the effectiveness of our symmetry-guided, multi-level contextual fusion paradigm for structural health monitoring. Full article

Pavement condition assessment using computer vision has emerged as an efficient alternative to traditional manual surveys, which are often labor-intensive and time-consuming. Leveraging deep learning, pavement distress such as cracks can be automatically detected, segmented, and quantified from high-resolution images captured by survey vehicles. Although numerous segmentation models have been proposed to generate crack masks, they typically require extensive pixel-level annotations, leading to high labeling costs. To overcome this limitation, this study integrates the Segmentation Anything Model (SAM), which produces accurate segmentation masks from simple bounding box prompts while leveraging its zero-shot capability to generalize to unseen images with minimal retraining. However, since SAM alone is not an end-to-end solution, we incorporate YOLOv8 for automated crack detection, eliminating the need for manual box annotation. Furthermore, the framework applies local refinement techniques to enhance mask precision and employs Optical Character Recognition (OCR) to automatically extract embedded GPS coordinates for geospatial mapping. The proposed framework is empirically validated using open-source pavement images from Yamanashi, demonstrating effective automated detection, classification, quantification, and geospatial mapping of pavement cracks. The results support automated pavement distress mapping onto real-world road networks, facilitating efficient maintenance planning for road agencies. Full article