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\"Inspired from the legacy of the previous four 3DFEM conferences held in Delft and Athens as well as the successful 2018 AM3P conference held in Doha, the 2020 AM3P conference continues the pavement mechanics theme including pavement models, experimental methods to estimate model parameters, and their implementation in predicting pavement performance. The AM3P conference is organized by the Standing International Advisory Committee (SIAC), at the time of this publication chaired by Professors Tom Scarpas, Eyad Masad, and Amit Bhasin. Advances in Materials and Pavement Performance Prediction II includes over 111 papers presented at the 2020 AM3P Conference. The technical topics covered include: rigid pavements, pavement geotechnics, statistical and data tools in pavement engineering, pavement structures, asphalt mixtures, asphalt binders. The book will be invaluable to academics and engineers involved or interested in pavement engineering, pavement models, experimental methods to estimate model parameters, and their implementation in predicting pavement performance.\"-- Provided by publisher.

The primary purpose of this paper is to investigate the impact of traffic wander on road pavement life for application in connected and autonomous vehicles. Research shows that in autonomous vehicles, drivers often follow the same path, leading to significant pavement damage on specific, well-defined paths. The paper examined the impact of traffic wander on pavement life by analysing two different wander distributions: normal and uniform. Based on the estimated pavement life for various pavement structures, a model that predicts the increase in pavement life due to traffic wander was developed for cracking and rutting prediction. The result of the research is the determination of relative pavement life influence functions, in which the variables are the traffic wander, asphalt layer thickness and subgrade stiffness. The obtained equations can be easily implemented for pavement service life extension evaluation. The model was also used to estimate the asphalt layer thickness as a function of the traffic expressed in terms of Equivalent Single Axle Load (ESALs). An analysis of the implications of the lateral distribution of traffic on the pavement thickness was presented. Significant reductions in the asphalt layer thickness of the pavement are achieved when wander is considered.

Cross-tensioned prestressed concrete pavement (CTPCP) has good integration and anti-crack performance with a high bearing capacity. An approximate model considering the effects of the sliding layer is developed to predict the longitudinal prestress of CTPCP, in which a bilinear model is used to describe the performance of the sliding layer. Additionally, a numerical simulation model is also developed to verify and modify the analytical model. Furthermore, the influence on longitudinal prestress has been analyzed according to the modified analytical model. The results show that the performance of the sliding layer has significant influence on longitudinal stress. In the ending area, the longitudinal prestress increases gradually with the increase of prestressed strands. In other areas, the longitudinal stress remains unchanged when the frictional coefficient of the sliding layer is ignored, while it decreases gradually and exists at a minimal value at the pavement midpoint when the friction effect of the sliding layer is taken into consideration. The angle and spacing of cross-tensioned prestressed strands also have significant influence on longitudinal prestress. Decreasing the angle and spacing can effectively improve the longitudinal prestress. Keywords: analytical model; cross-tensioned prestressed concrete pavement (CTPCP); effective stresses; numerical simulation; pavement engineering.

Pavement crack detection is of significant importance in ensuring road safety and smooth traffic flow. However, pavement cracks come in various shapes and forms which exhibit spatial continuity, and algorithms need to adapt to different types of cracks while preserving their continuity. To address these challenges, an innovative crack detection framework, CrackDiff, based on the generative diffusion model, is proposed. It leverages the learning capabilities of the generative diffusion model for the data distribution and latent spatial relationships of cracks across different sample timesteps and generates more accurate and continuous crack segmentation results. CrackDiff uses crack images as guidance for the diffusion model and employs a multi-task UNet architecture to predict mask and noise simultaneously at each sampling step, enhancing the robustness of generations. Compared to other models, CrackDiff generates more accurate and stable results. Through experiments on the Crack500 and DeepCrack pavement datasets, CrackDiff achieves the best performance (F1 = 0.818 and mIoU = 0.841 on Crack500, and F1 = 0.841 and mIoU = 0.862 on DeepCrack).

This paper proposes a performance-based mix design (PBMD) framework to support performance-related specifications (PRS) needed to establish relationships between acceptable quality characteristics (AQCs) and predicted performance, as well as to develop fatigue-preferred, rutting-preferred, and performance-balanced mix designs. The framework includes defining performance tests and threshold values, developing asphalt mix designs, identifying available performance levels, conducting sensitivity analysis, establishing the relationships between AQCs and predicted performance, and determining performance targets and AQC values for the three PBMDs using predicted performance criteria. Additionally, the framework recommends selecting the PBMD category for each asphalt layer to minimize pavement distresses. In this study, the proposed PBMD protocol was applied to FHWA accelerated loading facility (ALF) materials using asphalt mixture performance tester (AMPT) equipment coupled with mechanistic models. The study developed nine mix designs with varying design VMAs and air voids using the Bailey method. The cracking and rutting performance of the mix designs were determined by direct tension cyclic (DTC) fatigue testing, triaxial stress sweep (TSS) testing, and viscoelastic continuum damage (S-VECD) and viscoplastic shift models for temperature and stress effects. The study found that adjusting the design VMA was the primary way to achieve required performance targets. For fatigue-preferred mix design, the recommended targets were a cracking area of 0 to 1.9%, a rut depth of 10 mm, and a design VMA of 14.6 to 17.6%. For rutting-preferred mix design, the recommended targets were a cracking area of 18%, a rut depth of 0 to 3.8 mm, and a design VMA of 10.1 to 13.1%. For performance-balanced mix design, the recommended targets were a cracking area of 8.1 to 10.7%, a rut depth of 4.6 to 6.4 mm, and a design VMA of 12.6 to 14.3%. Finally, pavement simulation results verified that the proposed PBMD pavement design with fatigue-preferred mix in the bottom layer, performance-balanced mix in the intermediate layer, and rutting-preferred mix in the surface mix could minimize bottom-up cracking propagation without exceeding the proposed rutting performance criterion for long-life.

Pavement damage is the main factor affecting road performance. Pavement cracking, a common type of road damage, is a key challenge in road maintenance. In order to achieve an accurate crack classification, segmentation, and geometric parameter calculation, this paper proposes a method based on a deep convolutional neural network fusion model for pavement crack identification, which combines the advantages of the multitarget single-shot multibox detector (SSD) convolutional neural network model and the U-Net model. First, the crack classification and detection model is applied to classify the cracks and obtain the detection confidence. Next, the crack segmentation network is applied to accurately segment the pavement cracks. By improving the feature extraction structure and optimizing the hyperparameters of the model, pavement crack classification and segmentation accuracy were improved. Finally, the length and width (for linear cracks) and the area (for alligator cracks) are calculated according to the segmentation results. Test results show that the recognition accuracy of the pavement crack identification method for transverse, longitudinal, and alligator cracks is 86.8%, 87.6%, and 85.5%, respectively. It is demonstrated that the proposed method can provide the category information for pavement cracks as well as the accurate positioning and geometric parameter information, which can be used directly for evaluating the pavement condition.

In this paper, the effectiveness of reinforcements for flexible pavements is evaluated through an analysis of reflective cracking. Different stiffness and thickness reinforcements are considered for the rehabilitation of an already cracked pavement. The effect of the reinforcement is assessed from two different perspectives: (i) the ability to reduce stresses in the rehabilitated pavement layers, and (ii) the capacity to mitigate the crack propagation from deeper layers. A finite element model (FEM) is adopted to study the stress and strain state of the pavement layers. The pavement model has been properly validated, transitioning from a simply supported beam scheme to an elastic multilayer model. In addition, to represent crack propagation, fracture evolution is analyzed using Linear Elastic Fracture Mechanics (LEFMs) and Paris’ law. The effect of different reinforcements on the pavement is then simulated. The results show that the reinforcement performance is strictly dependent on the interlayer thickness and stiffness. In particular, high stiffness reinforcements (geomembranes) show increasing effectiveness with stiffness, both in terms of reflective cracking and stress reduction. Conversely, low stiffness reinforcements (SAMIs) show a variable trend with the stiffness modulus. In fact, extremely low stiffness is effective in slowing down crack propagation but is detrimental to the wearing course’s stress condition. However, as the stiffness increases, the likelihood of cracking in the wearing course decreases, though only a small beneficial effect is registered for crack propagation in the base layer.

Over recent years, significant research has been conducted to investigate ways to predict fatigue cracking and permanent deformation (rutting), which are two common distresses found in asphalt pavements. These distresses are affected by material properties, environmental conditions, and the pavement’s structure. This paper investigates common pavement design parameters, including surface mixture type, base layer thickness, base layer type, sub-base layer thickness, and an anti-frost layer, with regard to the asphalt pavement performance of the Korea Expressway Corporation (KEC) test road. Test roads are often regarded as the most realistic tools for evaluating the effects of various parameters because they are subjected to real traffic and environmental factors. The KEC test road is 7.7 km long and was constructed with the aim of developing a Korean mechanistic-empirical pavement design guide. According to the findings, the surface layer type, base layer thickness, and base layer material type were found to affect the fatigue cracking and rutting performance, whereas the sub-base thickness and anti-frost layer were found not to affect the amount of distress significantly. The newly developed ‘layered viscoelastic pavement analysis for critical distresses’ (LVECD) program was able to capture the effects of the changes in the aforementioned parameters on the amount of cracking and rut depths. Reasonable agreement was found between the LVECD predictions and the field distress measurements. However, it remains necessary to develop a laboratory-to-field transfer function in order to obtain more accurate field performance predictions.

In this paper, three different rheological models including a newly developed formulation based on the current Christensen Anderson and Marateanu (CAM) model, named sigmoidal CAM model (SCM), are used to estimate the evolution of roughness, rutting, and reflective cracking in a typical composite pavement structure currently widely adopted in South Korea. Three different asphalt mixtures were prepared and dynamic modulus tests were performed. Then, the mechanistic-empirical pavement design guide (MEPDG) was used for predicting the progression of the pavement distress and to estimate the effect of the three different models on such phenomena. It is found that the three different mathematical models provide lower and upper limits for roughness, rutting, and reflective cracking. While the CAM model may not be entirely reliable due to its inability in fitting the data in the high-temperature domain, SCM might result in moderately more conservative pavement design.

Pavement cracks are the primary type of distress that cause road damage, and deep-learning-based pavement crack segmentation is a critical technology for current pavement maintenance and management. To address the issues of segmentation discontinuity and poor performance in the segmentation of irregular cracks faced by current semantic segmentation models, this paper proposes an irregular pavement crack segmentation method based on multi-scale convolutional attention aggregation. In this approach, GhostNet is first introduced as the model backbone network for reducing parameter count, with dynamic convolution enhancing GhostNet’s feature extraction capability. Next, a multi-scale convolutional attention aggregation module is proposed to cause the model to focus more on crack features and thus improve the segmentation effect on irregular cracks. Finally, a progressive up-sampling structure is used to enrich the feature information by gradually fusing feature maps of different depths to enhance the continuity of segmentation results. The experimental results on the HGCrack dataset show that GMDNet has a lighter model structure and higher segmentation accuracy than the mainstream semantic segmentation algorithms, achieving 75.16% of MIoU and 84.43% of F1 score, with only 7.67 M parameters. Therefore, the GMDNet proposed in this paper can accurately and efficiently segment irregular cracks on pavements that are more suitable for pavement crack segmentation scenarios in practical applications.