Search Results (127)

The fatigue resistance of cement-stabilized macadam (CSM) plays a vital role in ensuring the long-term durability of pavement structures. However, limited cementitious material (CM) content often leads to high packing voids, which significantly compromise fatigue performance. Existing studies have rarely explored the coupled mechanism between pore structure and fatigue behavior, especially in the context of solid-waste-based CMs. In this study, a cost-effective alkali-activated sludge gasification slag (ASS) was proposed as a sustainable CM substitute for ordinary Portland cement (OPC) in CSM. A dual evaluation approach combining cross-sectional image analysis and fatigue loading tests was employed to reveal the effect pathway of void structure optimization on fatigue resistance. The results showed that ASS exhibited excellent cementitious reactivity, forming highly polymerized C-A-S-H/C-S-H gels that contributed to a denser microstructure and superior mechanical performance. At a 6% binder dosage, the void ratio of ASS–CSM was reduced to 30%, 3% lower than that of OPC–CSM. The 28-day unconfined compressive strength and compressive resilient modulus reached 5.7 MPa and 1183 MPa, representing improvements of 35.7% and 4.1% compared to those of OPC. Under cyclic loading, the ASS system achieved higher energy absorption and more uniform stress distribution, effectively suppressing fatigue crack initiation and propagation. Moreover, the production cost and carbon emissions of ASS were 249.52 CNY/t and 174.51 kg CO₂e/t—reductions of 10.9% and 76.2% relative to those of OPC, respectively. These findings demonstrate that ASS not only improves fatigue performance through pore structure refinement but also offers significant economic and environmental advantages, providing a theoretical foundation for the large-scale application of solid-waste-based binders in pavement engineering. Full article

Efficient and accurate recognition of highway pavement cracks is crucial for the timely maintenance and long-term use of expressways. Among the existing crack acquisition methods, human-based approaches are inefficient, whereas carrier-based automated methods are expensive. Additionally, both methods present challenges related to traffic obstruction and safety risks. To address these challenges, we propose a fixed pan-tilt-zoom (PTZ) vision-based highway pavement crack recognition workflow. Pavement cracks often exhibit complex textures with blurred boundaries, low contrast, and discontinuous pixels, leading to missed and false detection. To mitigate these issues, we introduce an algorithm named contrast-enhanced feature reconstruction (CEFR), which consists of three parts: comparison-based pixel transformation, nonlinear stretching, and generating a saliency map. CEFR is an image pre-processing algorithm that enhances crack edges and establishes uniform inner-crack characteristics, thereby increasing the contrast between cracks and the background. Extensive experiments demonstrate that CEFR improves recognition performance, yielding increases of 3.1% in F1-score, 2.6% in mAP@0.5, and 4.6% in mAP@0.5:0.95, compared with the dataset without CEFR. The effectiveness and generalisability of CEFR are validated across multiple models, datasets, and tasks, confirming its applicability for highway maintenance engineering. Full article

Foamed asphalt cold recycled mixtures can provide an effective approach for the reutilization of reclaimed asphalt pavement (RAP), but conventional asphalt foaming technology primarily exploits matrix asphalt as the raw material. To address this issue, this study explores rubberized asphalt with cold recycling technology to develop a foamed rubber asphalt cold recycled mixture (FRCM). The semi-circular bending (SCB) test was employed to investigate its cracking resistance. Load–crack mouth opening displacement (CMOD)–time curves under various temperatures were analyzed, and digital image technique was resorted to monitor crack propagation and growth rates. Fracture toughness, fracture energy, and flexibility index were compared with those of traditional foamed matrix asphalt cold recycled mixture (FMCM). The results show that, under the same test temperature, the FRCM exhibits slower crack propagation; larger peak load; and higher fracture toughness, fracture energy, and flexibility index in comparison with the FMCM. These improvements are more pronounced at low temperatures. For both mixtures, fracture toughness and fracture energy are decreased with increasing the temperature, while the flexibility index shows the opposite trend. The rigid zone accounts for a larger portion of fracture energy at low temperatures. The findings provide technical references for improving the cracking resistance of cold recycled asphalt layers using rubberized asphalt. Full article

Accurate segmentation of pavement cracks from high-resolution remote sensing imagery plays a crucial role in automated road condition assessment and infrastructure maintenance. However, crack structures often exhibit asymmetry, irregular morphology, and multi-scale variations, posing significant challenges to conventional CNN-based methods in real-world environments. Specifically, the proposed ETAFHrNet focuses on two predominant pavement-distress morphologies—linear cracks (transverse and longitudinal) and alligator cracks—and has been empirically validated on their intersections and branching patterns over both asphalt and concrete road surfaces. In this work, we present ETAFHrNet, a novel attention-guided segmentation network designed to address the limitations of traditional architectures in detecting fine-grained and asymmetric patterns. ETAFHrNet integrates Transformer-based global attention and multi-scale hybrid feature fusion, enhancing both contextual perception and detail sensitivity. The network introduces two key modules: the Efficient Hybrid Attention Transformer (EHAT), which captures long-range dependencies, and the Cross-Scale Hybrid Attention Module (CSHAM), which adaptively fuses features across spatial resolutions. To support model training and benchmarking, we also propose QD-Crack, a high-resolution, pixel-level annotated dataset collected from real-world road inspection scenarios. Experimental results show that ETAFHrNet significantly outperforms existing methods—including U-Net, DeepLabv3+, and HRNet—in both segmentation accuracy and generalization ability. These findings demonstrate the effectiveness of interpretable, multi-scale attention architectures in complex object detection and image classification tasks, making our approach relevant for broader applications, such as autonomous driving, remote sensing, and smart infrastructure systems. Full article

Crack sealant is commonly used to fill pavement cracks and improve the Pavement Condition Index (PCI). However, during asphalt pavement hot in-place recycling (HIR), irregular shapes and random distribution of crack sealants can cause issues like agglomeration and ignition. To address these problems, it is necessary to mill large areas containing crack sealant or pre-mark locations for removal after heating. Currently, detecting and recording crack sealant locations, types, and distributions is conducted manually, which significantly reduces efficiency. While deep learning-based object detection has been widely applied to distress detection, crack sealants present unique challenges. They often appear as wide black patches that overlap with cracks and potholes, and complex background noise further complicates detection. Additionally, no dataset specifically for crack sealant detection currently exists. To overcome these challenges, this paper presents a specialized dataset created from 1983 pavement images. A deep learning detection algorithm named YOLO-CS (You Only Look Once Crack Sealant) is proposed. This algorithm integrates the RepViT (Representation Learning with Visual Tokens) network to reduce computational complexity while capturing the global context of images. Furthermore, the DRBNCSPELAN (Dilated Reparam Block with Cross-Stage Partial and Efficient Layer Aggregation Networks) module is introduced to ensure efficient information flow, and a lightweight shared convolution (LSC) detection head is developed. The results demonstrate that YOLO-CS outperforms other algorithms, achieving a precision of 88.4%, a recall of 84.2%, and an mAP (mean average precision) of 92.1%. Moreover, YOLO-CS significantly reduces parameters and memory consumption. Integrating Artificial Intelligence-based algorithms into HIR significantly enhances construction efficiency. 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

Roads are critical infrastructure in multi-sectoral development. Any country that aims to expand and stabilize its activities must have a network of paved roads in good condition. However, that is not the case in many countries. The usual methods of recording and classifying pavement distress on the roads require a lot of equipment, technicians, and time to obtain the nature and indices of the damage to estimate the roadway’s quality level. This study proposes the use of pavement distress detection and classification models based on Convolutional Neural Networks, starting from videos taken of any asphalt road. To carry out this work, various routes were filmed to list the degradations concerned. Images were extracted from these videos and then resized and annotated. Then, these images were used to constitute several databases of road damage, such as longitudinal cracks, alligator cracks, small potholes, and patching. Within an appropriate development environment, three Convolutional Neural Networks were developed and trained on the databases. The accuracy achieved by the different models varies from 94.6% to 97.3%. This accuracy is promising compared to the literature models. This method would make it possible to considerably reduce the financial resources used for each road data campaign. Full article

Conventional micro-surfacing materials often delaminate, crack, or peel. These defects shorten pavement life. High-performance polymer-modified mixtures are essential for rapid pavement maintenance. We added waterborne epoxy resin (WER) at different dosages to styrene–butadiene rubber (SBR) to create a composite-modified micro-surfacing mixture. A series of laboratory comparative tests were conducted to investigate the effect of WER content on the overall performance of the WER-SBR micro-surfacing mixture. In addition, the microstructure of the mixtures was observed to analyze the mechanism by which the composite-modified emulsified asphalt enhances material performance, and the optimal WER dosage was determined. The results showed that higher WER content improved abrasion and rutting resistance but gains plateaued above 6% WER. Below 9% WER, mixtures showed good water stability; at 3–6% WER, they also maintained skid and low-temperature crack resistance. Notably, when the WER content was approximately 6%, the WER-SBR micro-surfacing mixture showed significantly reduced abrasion damage after exposure to freeze–thaw cycles, moisture, and salt spray conditions. SEM images confirmed that 6% WER creates a uniform asphalt film over aggregates, boosting mixture performance. Therefore, we recommend 6% WER. This study has developed a WER-SBR composite-modified emulsified asphalt micro-surfacing product with excellent overall performance. It holds significant practical value for extending pavement service life and improving road service quality. Full article

Pavement crack detection is crucial for ensuring road safety and reducing maintenance costs. Existing methods typically use convolutional neural networks (CNNs) to extract multi-level features from pavement images and employ attention mechanisms to enhance global features. However, the fusion of low-level features introduces substantial interference, leading to low detection accuracy for small-scale cracks with subtle local structures and varying global morphologies. In this paper, we propose a computationally efficient deep learning network with CNNs and multi-scale attention for multi-scale crack detection, named Crack-MsCGA. In this network, we avoid fusing low-level features to reduce noise interference. Then, we propose a multi-scale attention mechanism (MsCGA) to learn local detail features and global features from high-level features, compensating for the reduced detailed information. Specifically, first, MsCGA employs local window attention to learn short-range dependencies, aggregating local features within each window. Second, it applies a cascaded group attention mechanism to learn long-range dependencies, extracting global features across the entire image. Finally, it uses a multi-scale attention fusion strategy based on Mixed Local Channel Attention (MLCA) selectively to fuse local features and global features of pavement cracks. Compared with five existing methods, it improves the AP@50 by 11.3% for small-scale, 8.1% for medium-scale, and 5.9% for large-scale detection over the state-of-the-art methods in the DH807 dataset. Full article

Road crack detection is crucial for ensuring pavement safety and optimizing maintenance strategies. This study investigated the impact of image preprocessing methods and dataset balance on the performance of YOLOv8s-based crack detection. Four datasets (CFD, Crack500, CrackTree200, and CrackVariety) were evaluated using three image formats: RGB, grayscale (five conversion methods), and binarized images. The experimental results indicate that RGB images consistently achieved the highest detection accuracy, confirming that preserving color-based contrast and texture information benefits YOLOv8’s feature extraction. Grayscale conversion showed dataset-dependent variations, with different methods performing best on different datasets, while binarization generally degraded detection accuracy, except in the balanced CrackVariety dataset. Furthermore, this study highlights that dataset balance significantly impacts model performance, as imbalanced datasets (CFD, Crack500, CrackTree200) led to biased predictions favoring dominant crack classes. In contrast, CrackVariety’s balanced distribution resulted in more stable and generalized detection. These findings suggest that dataset balance has a greater influence on detection accuracy than preprocessing methods. Future research should focus on data augmentation and resampling strategies to mitigate class imbalance, as well as explore multi-modal fusion approaches for further performance enhancements. Full article

This study quantitatively investigates the fatigue cracking behavior of ultra-large particle size asphalt mixture (LSAM-50) under coupled temperature and stress effects. Fatigue tests were conducted across temperatures ranging from −15 °C to 35 °C and stress levels (0.3–0.9 of splitting tensile strength), with crack evolution tracked in real time using digital image correlation (DIC). Key parameters, including main crack length, crack density, curvature, fractal dimension, and strain, were analyzed to characterize crack propagation. Results revealed a three-stage process: initiation, development, and acceleration to failure. Increasing temperature or stress level accelerated horizontal/vertical displacement rates, main crack expansion, and strain accumulation, while reducing crack density and fractal dimension. A fatigue prediction model, LgN = 9.741 − 1.213Lgε − 0.017T − 1.579S (R² = 0.954), was established, linking fatigue life (N) to strain (ε), temperature (T), and stress level (S). This model enables precise fatigue life estimation under varying environmental conditions. For instance, the model predicts a 60% reduction in fatigue life when temperature rises from 15 °C to 35 °C at S = 0.7, highlighting its utility in material selection for climate-resilient infrastructure, offering a critical tool for optimizing LSAM-50 in pavement design. By integrating DIC-derived crack metrics and mechanistic insights, this work not only enhances understanding of the fatigue cracking behavior of LSAM-50 but also provides valuable insights for the design and optimization of materials under varying environmental conditions. Full article

Flexible pavements are susceptible to distress when subjected to long-term vehicle loads and environmental factors, thereby reqsuiring appropriate maintenance. To overcome the hectic field data collection and traffic congestion problems, this paper presents an intelligent prediction system framework utilizing Extreme Gradient Boosting (XGboost) to predict two relevant functional indices: rutting deformation and cracks damage. The model framework considers multiple essential factors, such as traffic load, material characteristics, and climate data conditions, to predict rutting behavior and employs image data to classify cracks behavior. The Extreme Gradient Boosting (XGboost) algorithm exhibited good performance, achieving an R² value of 0.9 for rutting behavior and an accuracy of 0.91, precision of 0.92, recall of 0.9, and F1-score of 0.91 for cracks. Moreover, a comparative assessment of the framework model with prominent AI methodologies reveals that the XGboost model outperforms support vector machine (SVM), decision tree (DT), random forest (RF), and K-Nearest Neighbor (KNN) methods in terms of quality of the result. For rutting behavior, a SHAP (Shapley Additive Explanations) analysis was performed on the XGboost model to interpret results and analyze the importance of individual features. The analysis revealed that parameters related to load and environmental conditions significantly influence the model’s predictions. Finally, the proposed model provides more precise estimates of pavement performance, which can assist in optimizing budget allocations for road authorities and providing dependable guidance for pavement maintenance. Full article

The swift advancement in monitoring and evaluation systems for road pavement conditions highlights the crucial role that this field plays in ensuring the sustainability of roads. This, in turn, contributes to the growth and prosperity of nations and enables users to enjoy the highest levels of luxury and comfort. Despite numerous studies and ongoing research, finding the most precise and efficient monitoring systems to determine the type and severity of road defects, their causes, and appropriate treatments remains a challenge. This study proposes a system that employs a camera to create an application capable of evaluating road conditions with ease by taking images while driving over the road. Based on the results, the application was accurate in identifying road defects of different severity within the same category. The proposed method was compared to the Pavement Condition Index (PCI) method, and a significant match was found in determining the type and severity of each defect on the selected road sections. More clearly, the overall accuracy of detecting and classifying block cracks, alligator cracks, longitudinal cracks, and potholes was significant for detecting and classifying the patches. Full article

Roads are a form of critical infrastructure, influencing economic growth, mobility, and public safety. However, the management, monitoring, and maintenance of road networks remain a challenge, particularly given limited budgets and the complexity of assessing widespread infrastructure. This Special Issue on “Road Detection, Monitoring, and Maintenance Using Remotely Sensed Data” presents innovative strategies leveraging remote sensing technologies, artificial intelligence (AI), and non-destructive testing (NDT) to optimize road infrastructure assessment. The ten papers published in this issue explore diverse methodologies, including novel deep learning algorithms for road inventory, novel methods for pavement crack detection, AI-enhanced ground-penetrating radar (GPR) imaging for subsurface assessment, high-resolution optical satellite imagery for unpaved road assessment, and aerial orthophotography for road mapping. Collectively, these studies demonstrate the transformative potential of remotely sensed data for improving the efficiency, accuracy, and scalability of road monitoring and maintenance processes. The findings highlight the importance of integrating multi-source remote sensing data with advanced AI-based techniques to develop cost-effective, automated, and scalable solutions for road authorities. As the first edition of this Special Issue, these contributions lay the groundwork for future advancements in remote sensing applications for road network management. Full article

This study explores the combined effects of nanosilica (NS) and basalt fibers (BF) on the mechanical and microstructural properties of superabsorbent polymer (SAP)-modified concrete. NS (0–1.5% replaced by cement weight) and BF (0–1.2% by volume fraction) were incorporated to optimize compressive, flexural, and split-tensile strengths using response surface methodology. Digital Image Correlation (DIC) was employed to analyze failure mechanisms. Results show that while SAP alone reduced strength, the addition of NS and BF mitigated this loss through synergistic microstructure enhancement and crack-bridging reinforcement. The optimal mix (0.9% NS and 1.2% BF) increased compressive, flexural, and split-tensile strengths by 15.3%, 10.0%, and 14.0%, respectively. SEM analysis revealed that NS filled SAP-induced pores, while BF limited crack propagation, contributing to improved mechanical strength of SAP-modified concrete. This hybrid approach offers a promising solution for durable and sustainable concrete pavements. Full article