Search Results (31)

Structural cracks are internal distresses that cannot be observed from pavement surfaces. However, the existing evaluation methods for asphalt pavement structures lack the consideration of these cracks, which are crucial for accurate pavement assessment and effective maintenance planning. This study develops a novel framework combining a three-dimensional (3D) ground penetrating radar (GPR) and finite element modeling (FEM) to evaluate the severity of structural cracks. First, the size and depth development of structural cracks on a four-layer asphalt pavement were determined using the 3D GPR. Then, the range of influence of the structural crack on structural bearing capacity was analyzed based on 3D FEM simulation model. Structural cracks have a distance-dependent diminishing influence on the deflection in the horizontal direction, with the most pronounced effects within a 20-cm width zone surrounding the cracks. Finally, two indices have been proposed: the pavement structural crack index (PSCI) to assess the depth of crack damage and the structural crack reflection ratio (SCRR) to evaluate surface reflection. Besides, PSCI and SCRR are used to classify the severities of structural cracks: none, low, and high. The threshold between none/low damage is a structural crack damage rate of 0.19%, and the threshold between low/high damage is 0.663%. An experiment on a 132-km expressway indicated that the proposed method achieved 94.4% accuracy via coring. The results also demonstrate the strong correlation between PSCI and pavement deflection (R² = 0.92), supporting performance-based maintenance strategies. The results also demonstrate the correlation between structural and surface cracks, with 65.8% of the cracked sections having both structural and surface cracks. Full article

Due to high temperatures and repeated load, asphalt pavements commonly experience rutting distress, a challenge that can be considerably reduced by modifying the binder components. This research focused on evaluating the performance of asphalt binders with single-use masks (SUMs) when subjected to high temperatures. For this purpose, dynamic shear rheometer (DSR)-based frequency sweep, temperature sweep, and multiple stress creep recovery (MSCR) experiments were performed on various asphalt binders, including both unmodified and SUM-modified (SUMM) samples. To explore the effects of temperature, SUM content, and loading frequency on the rutting performance of the SUMM samples, a statistical modeling-based response surface methodology (RSM) was utilized, enabling the creation of predictive mathematical models. To investigate the internal morphology of the binders, fluorescence microscopy (FM) was applied. Data from rheological analyses revealed that the addition of SUM markedly boosted the high-temperature resistance of the asphalt binder. Findings from the MSCR analysis indicated that the SUMM samples achieved lower nonrecoverable compliance (J_nr) and greater percent recovery (R) values than the reference asphalt, suggesting that SUMs significantly enhance the binder’s resistance to rutting. Data analysis demonstrated that the chosen independent variables had a considerable effect on the asphalt’s complex modulus (G*) and rutting performance (G*/sin (δ)), prompting the formulation of predictive models for rutting susceptibility. Moreover, the FM examination demonstrated that the SUM was homogeneously integrated across the asphalt matrix. Full article

Pavement surface distresses are analyzed by transportation agencies to determine section performance across their pavement networks. To efficiently collect and evaluate thousands of lane-miles, automated processes utilizing image-capturing techniques and detection algorithms are applied to perform these tasks. However, the precision of this novel technology often leads to inaccuracies that must be verified by pavement engineers. Developments in artificial intelligence and machine learning (AI/ML) can aid in the progress of more robust and precise detection algorithms. Deep learning models are efficient for visual distress identification of pavement. With the use of 2D/3D pavement images, surface distress analysis can help train models to efficiently detect and classify surface distresses that may be caused by traffic loading, weather, aging, and other environmental factors. The formation of these distresses is developing at a higher rate in coastal regions, where extreme weather phenomena are more frequent and intensive. This study aims to develop a YOLOv5 model with 2D/3D images collected in the states of Louisiana, Mississippi, and Texas in the U.S. to establish a library of data on pavement sections near the Gulf of Mexico. Images with a resolution of 4096 × 2048 are annotated by utilizing bounding boxes based on a class list of nine distress and non-distress objects. Along with emphasis on efforts to detect cracks in the presence of background noise on asphalt pavements, six scenarios for augmentation were made to evaluate the model’s performance based on flip probability in the horizontal and vertical directions. The YOLOv5 models are able to detect defined distresses consistently, with the highest mAP50 scores ranging from 0.437 to 0.462 throughout the training scenarios. Full article

There is a growing global interest in preserving transportation infrastructure. This necessitates routine evaluation and timely maintenance of road networks. The effectiveness of pavement management systems (PMSs) heavily relies on accurate pavement deterioration models. However, there are limited comparative studies on modeling approaches for rural roads in arid climatic conditions using the same datasets for training and testing. This study compares three approaches for developing a pavement condition index (PCI) model as a function of pavement age: classical regression, machine learning, and deep learning. The PCI is a pavement management index widely adopted by many road agencies. A dataset on pavement age and distress was collected over a twenty-year period to develop reliable predictive models. The results demonstrate that the regression model, machine learning model, and the deep learning model achieved a coefficient of determination ($R^2$) of 0.973, 0.975, and 0.978, respectively. While these values are technically equal, the average bias for the deep learning model (1.14) was significantly lower than that of the other two models, signaling its superiority. Additionally, the trend predicted by the deep learning model showed more distinct phases of PCI deterioration with age than the machine learning model. The latter exhibited a wider range of PCI deterioration rates over time compared to the regression model. The deep learning model outperforms a recently developed regression model for a similar region. These findings highlight the potential of using deep learning to estimate pavement surface conditions accurately and its efficacy in capturing the PCI-age relationship. Full article

The advantages of asphalt pavement in terms of driving comfort, construction efficiency, and ease of maintenance have established it as the predominant choice for high-grade pavements at present. However, being highly sensitive to temperature and stress, asphalt performance is significantly influenced by external environmental conditions and loading, making it susceptible to various distress phenomena. Particularly in high-latitude regions, asphalt pavement cracking severely limits asphalt pavement’s functional performance and service lifespan under cold climatic conditions. To enhance the low-temperature cracking resistance of asphalt pavement in cold regions, tools such as VOS viewer 1.6.20 and Connected Papers were utilized to systematically organize, analyze, and summarize relevant research from the past 40 years. The results reveal that temperature shrinkage cracks and thermal fatigue cracks represent the primary forms of asphalt pavement distress in these regions. Cracking in asphalt pavement in cold regions is primarily influenced by structural design, pavement materials, construction technology, and climatic conditions. Among these factors, surface layer stiffness, base layer type, and the rate of temperature decrease exert the most significant impact on cracking resistance, collectively accounting for approximately 45.4% of all cracking-related factors. The low-temperature performance of asphalt pavement can be effectively improved through several strategies, including adopting full-thickness asphalt pavement with a skeleton-dense structure or reduced average particle size, incorporating functional layers, appropriately increasing the thickness of the upper layer and the compaction temperature of the lower layer, utilizing continuous surface layer construction techniques, and applying advanced materials. High-performance modifiers such as SBR and SBS, nanomaterials with good low-temperature performance, and warm mixing processes designed for cold regions have proven particularly effective. Among various improvement methods, asphalt modification has demonstrated superior effectiveness in enhancing the deformation capacity of asphalt and its mixtures, significantly boosting the low-temperature performance of asphalt pavements. Asphalt modification accounts for approximately 50% of the improvement methods evaluated in this study, with an average improvement in low-temperature performance reaching up to 143%. This paper provides valuable insights into the underlying causes of cracking distress in asphalt pavements in cold regions and offers essential guidance for improving the service quality of such pavements in these challenging environments. Full article

Asphalt pavements are subject to regular inspection and maintenance activities over time. Many techniques have been suggested to evaluate pavement surface conditions, but most of these are either labour-intensive tasks or require costly instruments. This article describes a robust intelligent pavement distress inspection system that uses cost-effective equipment and the ‘you only look once’ detection algorithm (YOLOv3). A dataset for flexible pavement distress detection with around 13,135 images and 30,989 bounding boxes of damage was used during the neural network training, calibration, and validation phases. During the testing phase, the model achieved a mean average precision of up to 80%, depending on the type of pavement distress. The performance metrics (loss, precision, recall, and RMSE) that were applied to estimate the object detection accuracy demonstrate that the technique can distinguish between different types of asphalt pavement damage with remarkable accuracy and precision. Moreover, the confusion matrix obtained in the validation process shows a distress classification sensitivity of up to 98.7%. The suggested technique was successfully implemented in an inspection car. Measurements conducted on urban roads crossed by tramway lines in the city of Palermo proved the real-time ability and great efficacy of the detection system, with potentially remarkable advances in asphalt pavement examination efficacy due to the high rates of correct distress detection. Full article

Before conducting pavement rehabilitation, the concrete pavement performance evaluation is more than necessary to assess the strength and analyze the internal condition. To evaluate the modulus performance of concrete pavement and provide theoretical support for subsequent maintenance and rehabilitation, the SASW (Spectral-Analysis-of-Surface-Waves) method, a non-destructive testing method based on surface waves, was used to determine the modulus of three in-service concrete pavements. At the same time, FWD (Falling Weight Deflectometer) testing and on-site coring were carried out at the measuring points. Through the analysis of modulus results measured in the field, it was found that the SASW method can accurately obtain the elastic modulus of the concrete layer and base layer. Compared with the concrete modulus measured by FWD, the modulus measured by the SASW method was closer to the uniaxial compressive modulus. Moreover, since the SASW method has the ability to provide gradient modulus varying with depth and determine the pavement thicknesses, it is reasonable to recognize the location of the inherent distress based on the region where the modulus suddenly decreases within one layer. The comprehensive evaluation of concrete pavement condition, including the modulus value and internal distress recognition, has the capability to assist in determining the design of overlay asphalt thickness and material. Full article

Road surface deterioration, such as cracks and potholes, poses a significant threat to both road safety and infrastructure longevity. Swift and accurate detection of these issues is crucial for timely maintenance and user security. However, current techniques often overlook the unique characteristics of pavement images, where the small distressed areas are vastly outnumbered by the background. In response, we propose an innovative road distress classification model that capitalizes on sparse perception. Our method introduces a sparse feature extraction module using dilated convolution, tailored to capture and combine sparse features of different scales from the image. To further enhance our model, we design a specialized loss function rooted in domain-specific knowledge about pavement distress. This loss function enforces sparsity during feature extraction, guiding the model to align precisely with the sparse distribution of target features. We validate the strength and effectiveness of our model through comprehensive evaluations of a diverse dataset of road images containing various distress types and conditions. Our approach exhibits significant potential in advancing traffic safety by enabling more efficient and accurate detection and classification of road distress. Full article

The crack resistance of asphalt pavement mixtures directly impact pavement service condition and pavement distress. And characterizing the crack resistance of a pavement mixture can reflect the crack resistance potential of asphalt pavement. This study analyzes several representative highway sections based on time, material, and service conditions to identify the mixture type of three layers. Semi-circular bending tests are conducted at 15 °C, and load–displacement curves are recorded. Factor independence analysis is performed, and combinations showcasing the cracking performance of the surface layer, middle layer, and bottom layer are selected. Analysis of variance (ANOVA) evaluating the indices versus selected factors for the three layers identifies significant influencing factors, and the crack resistance is analyzed based on these significant factors. The crack resistance of the middle layer with the highest truck loads is significantly lower than the two other lanes and the shoulder. Transverse crack spacing (TCS) can be used to directly evaluate the crack resistance of the mixture. The Factor dots upper rate (FUDR) and absolute Factor dots upper rate (absFUDR) indices are introduced to quantify the percentage deviation of a factor specimen from the average crack resistance index–fracture energy ratio, indicating whether the crack curve becomes sharper or flatter. The factor dots upper rate index is then applied to characterize the factors, and the results are reasonable. It is found only on the surface and middle layers that the service age has significant impacts on crack resistance, the Transverse crack spacing has significant impacts on crack resistance index, and the Factor dots upper rate can identify the brittleness of mixtures with different factors. Full article

The main objective of this study is to simultaneously evaluate the surface and existing layers of composite pavement using PMS data and GPR equipment. The distribution of dielectric constants according to the existing concrete conditions and the relationship between dielectric constants and surface distress was evaluated. As a result, the dielectric constant distribution of the existing concrete showed a significant difference depending on the AAR. In addition, the correlation between surface distress and the dielectric constants of the existing layers was low. Full article

The extreme arid climatic conditions and poor asphalt mix design characteristics have further accelerated the rate of deterioration of in-service asphalt pavements in Kuwait. Pavement distresses, such as raveling, rutting, and fatigue cracks, worsen in severe climatic and loading conditions, such as high temperatures, elevated humidity levels, and high traffic loads on the pavement surfaces. In this study, a life performance evaluation using mechanistic–empirical analysis of a new modified Superpave mix design was undertaken. The performance life of the modified Superpave asphalt mixture was evaluated, and the results showed that the new modifications to the mix improved the rutting and fatigue cracking resistances of the asphalt mixture for the unconditioned state. However, fatigue cracking resistance under the moisture-conditioned state still needs further improvement for the newly modified Superpave asphalt mixture. Full article

Moisture damage is one of the undesired distresses occurring in flexible asphalt pavements, mostly through water intrusion that weakens and ultimately degrades the asphalt mortar-aggregate interfacial bond. One method to mitigate this distress is using anti-stripping or anti-spalling filler materials that, however, require a systematic quantification of their interfacial bonding potential and moisture tolerance properties prior to wide-scale field use. With this background, this study was conducted to comparatively evaluate and quantitatively characterize the moisture sensitivity and water damage resistance of the interfacial bonding between the asphalt mortar and aggregate fillers. Using an in-house custom developed water-temperature coupling setup, numerous laboratory pull-out tests were carried out on the asphalt mortar with four different filler materials, namely limestone mineral powder, cement, slaked (hydrated) lime, and waste brake pad powder, respectively. In the study, the effects of moisture wet-curing conditions, temperature, and filler types were comparatively evaluated to quantify the water damage resistance of the asphalt mortar-aggregate filler interface. For interfacial microscopic characterization, the Image-Pro Plus software, 3-D digital imaging, and scanning electron microscope (SEM) were jointly used to measure the spalling rate and the surface micromorphology of the asphalt mortar and aggregate filler before and after water saturation, respectively. In general, the pull-out tensile force exhibited a decreasing response trend with more water damage and interfacial bonding decay as the moisture wet-curing temperature and time were increased. Overall, the results indicated superiority for slaked (hydrated) lime over the other filler materials with respect to enhancing and optimizing the asphalt mortar-aggregate interfacial bonding strength, moisture tolerance, and water damage resistance, respectively—with limestone mineral powder being the poorest performer. Full article

An entire roadway system represents a crucial element in the sustainable urban transportation planning process. Pavement surfaces are at continual risk of accumulating serious deteriorations and defects throughout their service life due to traffic loading and environmental impact. Since roadway networks are growing rapidly, relying on visual pavement inspection is not always feasible. Therefore, this paper proposes an effective assessment method for evaluating flexible pavement surface distresses using a terrestrial laser scanner (TLS) and calculating the pavement condition index (PCI). The proposed terrestrial laser scanner method results in road condition assessments becoming faster, safer, and more systematic. It also aims to determine the geometric characteristics of the investigated roads. A major road in Egypt was selected to test the proposed technique and compare it with the traditional visual inspection method. The evaluation was carried out to assess different types of pavement distress, such as cracking, rutting, potholes, and raveling distresses. Every pavement distress was defined in terms of surface area, the width of the crack, and intensity, and the data from TLS were then processed by MAGNET COLLAGE software. A MATLAB program was developed to match the TLS observational data to plane equations. PAVER software was also used to determine the PCI values for each TLS position. The revealed distresses for the investigated road using TLS observations reveal a significant improvement in determining flexible pavement distresses and geometric characteristics. Full article

Traffic loads and environmental factors cause various forms of distress on road pavements (cracks, depressions, potholes, ruts, etc.). Depressions and ruts produce localized variations of longitudinal and cross slopes, which are very hazardous for drivers, especially during rain. In such conditions, these defects alter the surface water path, creating abnormal water accumulations and significant water film depths to induce aquaplaning risk. In current practice, in preliminary analysis phases and at the network scale, the control of road surfaces is carried out with expeditious techniques and with synthetic indicators, e.g., pavement condition index (PCI), through which a quality judgment related to the detected distresses on the pavement surface, is given. In truth, the detection of specific defects (ruts and depressions) should also include further analyses to evaluate the hydraulic efficiency of the carriageway related to their severity. Therefore, in this paper, a synthetic indicator called Hydraulic Condition Index (HCI) is proposed for evaluating the hydraulic quality of road pavement surfaces. This index is related to the hydrologic conditions of the site, the pavement characteristics, and the defects that can alter the flow of water on the carriageway, determining and increasing the risk of aquaplaning. The methodological framework is discussed by means of some numerical applications developed for different road typologies according to their functional classification. The final aim is to provide road agencies with another solution to evaluate road quality and ensure safer roads for users. The methodological framework for evaluating the HCI may be adopted by the road agencies for the network-scale priority ranking of road segments maintenance needs also involving safety traffic conditions. Full article

Road pavement condition assessment is essential for maintenance, asset management, and budgeting for pavement infrastructure. Countries allocate a substantial annual budget to maintain and improve local, regional, and national highways. Pavement condition is assessed by measuring several pavement characteristics such as roughness, surface skid resistance, pavement strength, deflection, and visual surface distresses. Visual inspection identifies and quantifies surface distresses, and the condition is assessed using standard rating scales. This paper critically analyzes the research trends in the academic literature, professional practices and current commercial solutions for surface condition ratings by civil authorities. We observe that various surface condition rating systems exist, and each uses its own defined subset of pavement characteristics to evaluate pavement conditions. It is noted that automated visual sensing systems using intelligent algorithms can help reduce the cost and time required for assessing the condition of pavement infrastructure, especially for local and regional road networks. However, environmental factors, pavement types, and image collection devices are significant in this domain and lead to challenging variations. Commercial solutions for automatic pavement assessment with certain limitations exist. The topic is also a focus of academic research. More recently, academic research has pivoted toward deep learning, given that image data is now available in some form. However, research to automate pavement distress assessment often focuses on the regional pavement condition assessment standard that a country or state follows. We observe that the criteria a region adopts to make the evaluation depends on factors such as pavement construction type, type of road network in the area, flow and traffic, environmental conditions, and region’s economic situation. We summarized a list of publicly available datasets for distress detection and pavement condition assessment. We listed approaches focusing on crack segmentation and methods concentrating on distress detection and identification using object detection and classification. We segregated the recent academic literature in terms of the camera’s view and the dataset used, the year and country in which the work was published, the F1 score, and the architecture type. It is observed that the literature tends to focus more on distress identification (“presence/absence” detection) but less on distress quantification, which is essential for developing approaches for automated pavement rating. Full article