Search Results (70)

The absence of standardized evaluation criteria for cold patch asphalt mixtures (CPAMs) leads to arbitrary material selection in pavement pothole repair, resulting in premature failure and recurrent damage. This study develops a comprehensive evaluation framework combining macro-performance tests with X-ray computed tomography (CT)-based meso-structural analysis. The macroscopic evaluation system incorporates six key parameters: aggregate gradation, binder–aggregate ratio, penetration strength, molding strength, residual rate, and stability retention. The CT-based meso-structural assessment quantifies void characteristics (longitudinal distribution, radial distribution, fractal dimension) and aggregate skeleton features (contact points, coordination number) through 3D reconstruction. Experimental results demonstrate that optimizing asphalt content (4.5–4.7%) with adjusted critical aggregate fractions (4.75 mm:35.0–45.0%; 2.36 mm:30.0–40.0%; 13.2 mm:1.0–1.2%; 9.5 mm:10.0–15.0%) significantly enhances repair durability. The established multiscale evaluation methodology provides a theoretical foundation for developing standardized CPAM quality specifications, particularly in emergency maintenance scenarios. 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

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

The accurate detection of road defects is crucial for enhancing the safety and efficiency of road maintenance. This study focuses on six common types of pavement defects: transverse cracks, longitudinal cracks, alligator cracking, oblique cracks, potholes, and repair marks. In real-world scenarios, key challenges include effectively distinguishing between the foreground and background, as well as accurately identifying small-sized (e.g., fine cracks, dense alligator cracking, and clustered potholes) and overlapping defects (e.g., intersecting cracks or clustered damage areas where multiple defects appear close together). To address these issues, this paper proposes a Pavement-DETR model based on the Real-Time Detection Transformer (RT-DETR), aiming to optimize the overall accuracy of defect detection. To achieve this goal, three main improvements are proposed: (1) the introduction of the Channel-Spatial Shuffle (CSS) attention mechanism in the third (S3) and fourth (S4) stages of the ResNet backbone, which correspond to mid-level and high-level feature layers, enabling the model to focus more precisely on road defect features; (2) the adoption of the Conv3XC structure for feature fusion enhances the model’s ability to differentiate between the foreground and background, which is achieved through multi-level convolutions, channel expansion, and skip connections, which also contribute to improved gradient flow and training stability; (3) the proposal of a loss function combining Powerful-IoU v2 (PIoU v2) and Normalized Wasserstein Distance (NWD) weighted averaging, where PIoU v2 focuses on optimizing overlapping regions, and NWD targets small object optimization. The combined loss function enables comprehensive optimization of the bounding boxes, improving the model’s accuracy and convergence speed. Experimental results show that on the UAV-PDD2023 dataset, Pavement-DETR improves the mean average precision (mAP) by 7.7% at IoU = 0.5, increases mAP by 8.9% at IoU = 0.5–0.95, and improves F1 Score by 7%. These results demonstrate that Pavement-DETR exhibits better performance in road defect detection, making it highly significant for road maintenance work. 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

The viscoelastic behavior of asphalt mixtures is a crucial consideration in the analysis of pavement mechanical responses and structural design. This study aims to elucidate the molecular structure and component evolution trends of polyphosphoric acid (PPA)/styrene butadiene styrene block copolymer (SBS)/styrene butadiene rubber copolymer (SBR) composite modified asphalt (CMA) under rolling thin film oven test (RTFOT) and pressure aging (PAV) conditions, as well as to analyze the viscoelastic evolution of CMA mixtures. First, accelerated aging was conducted in the laboratory through RTFOT, along with PAV tests for 20 h and 40 h. Next, the microscopic characteristics of the binder at different aging stages were explored using Fourier-transform infrared spectroscopy (FTIR) and gel permeation chromatography (GPC) tests. Additionally, fundamental rheological properties and temperature sweep tests were performed to reveal the viscoelastic evolution characteristics of CMA. Ultimately, the viscoelastic properties of CMA mixtures under dynamic loading at different aging stages were clarified. The results indicate that the incorporation of SBS and SBR increased the levels of carbonyl and sulfoxide factors while decreasing the level of long-chain factors, which slowed down the rate of change of large molecule content and reduced the rate of change of LMS by more than 6%, with the rate of change of overall molecular weight distribution narrowing to below 50%. The simultaneous incorporation of SBS and SBR into CMA mixtures enhanced the dynamic modulus in the 25 Hz and −10 °C range by 24.3% (AC-13), 15.4% (AC-16), and reduced the $\phi$ by 55.8% (AC-13), 40% (AC-16). This research provides a reference for the application of CMA mixtures in the repair of pavement pothole damage. Full article

Analytical models to predict distresses and service conditions of road pavements can greatly contribute to the development of an effective pavement management system. These models allow the transportation agencies to monitor and track the deterioration of pavements and consequently determine the needed maintenance operations to preserve the performance of the network. In this research, the pavement distresses and service conditions of the Indus Highway N-55 located in Karak district, Pakistan were examined. Distresses were identified by visual observation, and then their severity and extent were measured individually by using a Vernier caliper and a measuring scale. For each distress type, the corresponding PCR was calculated. The compaction densities of the base and wearing courses were considered as input parameters to develop an ordinal logistic regression model for two dominant distresses, namely rutting and potholes. Rutting severity and extent were divided into three levels, while pothole severity was divided into four levels. Bulk and maximum specific gravity were found to have a significant impact on the models of both distresses. The model can be used to predict their development in terms of severity and extent. The proposed formulation provides valuable insights into monitoring and predicting pavement distresses by assessing the densities of road construction materials. Full article

As part of the Horizon 2020 InfraROB project aimed at enhancing road safety through innovative robotic solutions, a compact autonomous vehicle has been developed to repair asphalt potholes. Central to this system is a 3D printer capable of extruding a novel cold-asphalt mixture, specifically designed for patching road surfaces. The printer is mounted on a small robot that autonomously navigates to potholes, while the human operator controls the operation from a secure location outside the traffic area. The system’s development involved several key steps: designing the repair mixture, constructing the 3D printer for mixture extrusion, implementing a photogrammetric technique to accurately measure pothole geometry for printing, and integrating the extrusion system with the robotic platform. Two preliminary tests were conducted in controlled environments at Sapienza University of Rome to check the reliability of calculation of the amount of material needed to fill in the potholes. Finally, the entire procedure was tested on an Italian motorway, demonstrating the system’s functionality without encountering operational issues. Full article

In order to reduce the amount of diluent in a diluted asphalt mixture, this study developed a cold patch asphalt (CPA) for repairing pavement potholes by using a mixture of treated biodiesel and diesel as the diluent. The effects of biodiesel on the performance of the cold patch asphalt mixture (CPAM) during the construction process were investigated through Brookfield rotational viscosity tests, adhesion tests, and FTIR (Fourier transform infrared spectroscopy) analyses. At the same time, the effect of biodiesel on the performance of the CPAM was analyzed by combining the strength growth test, rutting test, and water-soaked Marshall test of CPAMs. The test results show that the construction performance of the CPAM can be significantly improved by adding pretreated biodiesel. Under the same amount of diluent, the strength and high-temperature performance of the asphalt mixture diluted with biodiesel were significantly improved compared to that with diesel as the diluent. The optimal high-temperature performance reached 9027 (times/mm), representing an approximate increase of 94.7% compared to 4636 (times/mm) when only diesel was used as the diluent. When the biodiesel content increased from 10% to 40%, the residue stability improved from 85.9% to 91.3%. The corresponding 0.5 h Marshall stability increased from 5.59 kN to 8.1 kN, while the 48 h Marshall stability rose from 4.8 kN to 7.39 kN. All tests met the requirements for hot mix asphalt. Full article

This research examines the influence of various pavement types on vehicle dynamics, specifically concentrating on vertical acceleration and its implications for unsprung mass, including the wheels and suspension system. The objective of this project was to categorize pavement types with accelerometer data, enabling a deeper comprehension of the impact of road surface conditions on vehicle stability, comfort, and mechanical stress. Two categorization methods were utilized: a neural network and a multinomial logistic regression model. Accelerometer data were gathered while a car navigated diverse terrain types, such as grates, potholes, and cobblestones. The neural network model exhibited exceptional performance, with 100% accuracy in categorizing all surface types, while the multinomial logistic regression model reached 97.14% accuracy. The neural network demonstrated exceptional efficacy in differentiating intricate surface types such as potholes and grates, surpassing the logistic regression model which had difficulties with these surfaces. These results underscore the neural network’s effectiveness in the real-time categorization of road surfaces, enhancing the comprehension of vehicle dynamics influenced by pavement conditions. Future studies must tackle the difficulty of identifying analogous surfaces by enhancing methodologies or integrating more data attributes for greater precision. Full article

The escalating impacts of climate change have led to significant challenges in maintaining road infrastructure, particularly in tropical climates. Abnormal weather patterns, including increased precipitation and temperature fluctuations, contribute to the accelerated deterioration of asphalt pavements, resulting in cracks, plastic deformation, and potholes. This study aims to evaluate the durability of a novel pellet-type stripping prevention material incorporating slaked lime and epoxy resin for pothole restoration in tropical climates. The modified asphalt mixtures were subjected to a series of laboratory tests, including the Tensile Strength Ratio (TSR) test, Indirect Tension Strength (ITS) test, Hamburg Wheel Tracking (HWT) test, Cantabro test, and Dynamic Modulus test, to assess their moisture resistance, rutting resistance, abrasion resistance, and viscoelastic properties. Quantitative results demonstrated significant improvements in the modified mixture’s performance. The TSR test showed a 6.67% improvement in moisture resistance after 10 drying–wetting cycles compared to the control mixture. The HWT test indicated a 10.16% reduction in rut depth under standard conditions and a 27.27% improvement under double load conditions. The Cantabro test revealed a 44.29% reduction in mass loss, highlighting enhanced abrasion resistance. Additionally, the Dynamic Modulus test results showed better stress absorption and reduced likelihood of cracking, with the modified mixture demonstrating superior flexibility and stiffness under varying temperatures and loading frequencies. These findings suggest that the incorporation of slaked lime and epoxy resin significantly enhances the durability and performance of asphalt mixtures for pothole repair, making them a viable solution for sustainable road maintenance in tropical climates. Full article

This paper presents a novel multi-initialization model for recognizing road surface damage, e.g. potholes and cracks, on video using convolutional neural networks (CNNs) in real-time for fast damage recognition. The model is trained by the latest Road Damage Detection dataset, which includes four types of road damage. In addition, the CNN model is updated using pseudo-labeled images from semi-learned methods to improve the performance of the pavement damage detection technique. This study describes the use of the YOLO architecture and optimizes it according to the selected parameters, demonstrating high efficiency and accuracy. The results obtained can enhance the safety and efficiency of road pavement and, hence, its traffic quality and contribute to decision-making for the maintenance and restoration of road infrastructure. Full article

Colored asphalt pavements have been implemented in South Korea to enhance visibility and lane distinction; however, color fading, accelerated deterioration, and increased pothole occurrence have been noticed. As a solution, alternative materials that can be used for the construction of colored asphalt pavements are being explored. This study evaluates the feasibility of using mudstone aggregate in constructing colored asphalt pavement in South Korea. Initially, aggregate quality tests were conducted on mudstone samples to assess their suitability compared to standard criteria. To enhance the visibility and color retention of colored asphalt, addition of pigment in the colored asphalt pavement mixture was considered and evaluated. The asphalt mixtures were evaluated for deformation, crack and viscoelastic properties using the Kim test, indirect tensile (IDT) strength test, and dynamic modulus test, respectively. Results showed that mudstone aggregate exceeded quality standards and the colored asphalt mixtures demonstrated superior deformation strength and crack resistance compared to typical SMA. However, the addition of pigment slightly reduced these properties. Overall, the findings suggest mudstone aggregate as a viable alternative for constructing colored asphalt pavements, offering potential improvements in durability and color retention. Full article

Timely discovery and disposal of road risk sources constitute the cornerstone of road operation safety. Presently, the detection of road risk sources frequently relies on manual inspections via inspection vehicles, a process that is both inefficient and time-consuming. To tackle this challenge, this paper introduces a novel automated approach for detecting road risk sources, termed the multi-scale lightweight network (MSLN). This method primarily focuses on identifying road surfaces, potholes, and scattered objects. To mitigate the influence of real-world factors such as noise and uneven brightness on test results, pavement images were carefully collected. Initially, the collected images underwent grayscale processing. Subsequently, the median filtering algorithm was employed to filter out noise interference. Furthermore, adaptive histogram equalization techniques were utilized to enhance the visibility of cracks and the road background. Following these preprocessing steps, the MSLN model was deployed for the detection of road risk sources. Addressing the challenges associated with two-stage network models, such as prolonged training and testing times, as well as deployment difficulties, this study adopted the lightweight feature extraction network MobileNetV2. Additionally, transfer learning was incorporated to elevate the model’s training efficiency. Moreover, this paper established a mapping relationship model that transitions from the world coordinate system to the pixel coordinate system. This model enables the calculation of risk source dimensions based on detection outcomes. Experimental results reveal that the MSLN model exhibits a notably faster convergence rate. This enhanced convergence not only boosts training speed but also elevates the precision of risk source detection. Furthermore, the proposed mapping relationship coordinate transformation model proves highly effective in determining the scale of risk sources. Full article

With the rapid development of China’s transportation network, the demand for bridge construction is increasing, the traffic volume is increasing yearly, and the average vehicle speed and the frequency of overloaded vehicles crossing bridges are soaring. When a vehicle passes over a highway bridge, it can easily form a coupling vibration between the vehicle and bridge due to the excitation of the expansion joint, the unevenness of the bridge deck, and the existing coating-hole. The impact effect is significant, which seriously affects the operation safety of both the vehicle and bridge, seriously damaging the service life of the bridge. Due to the influence of construction technology, it is common for the vibration to meet transverse and longitudinal expansion joints of a prefabricated girder bridge, where an aging bridge deck frequently results in bulges and potholes in asphalt pavement. The bridge vibration amplification effect under the dynamic load of heavy, high-speed vehicles is significant, and research about the large impact coefficient of bridges with local pavement deterioration is urgently needed. This study used SIMULINK simulation software and involved conducting several bridge model tests. Dynamic simulation analyses and running vehicle tests on scaled and real bridge models were carried out to study the coupling vibration response of bridge decks in the presence of different pothole sizes. The results show that the impact effect of low-speed vehicles passing through a larger-sized pothole is relatively significant, and the impact coefficient can be amplified to 214% of the original value under good road surfaces in extreme cases. The vehicle–bridge coupling impact effect of potholes is similar to bulges. This relevant work could provide suggestions for the operational performance evaluation and maintenance of bridges with local pavement deterioration. Full article