Accurate identification of cracks at the pixel level on intricate asphalt pavements represents a crucial challenge in the domain of intelligent pavement assessment. The current advanced deep-learning networks encounter limitations in simultaneously capturing both the global context and local features of cracks, leading to discontinuous segmentation results and suboptimal recovery of local details. This paper proposes a robust architecture named Mix-Graph CrackNet to present an efficacious solution for this challenge. The Mix-Graph CrackNet, as proposed, is designed to mix the global context and local features multiple times, allowing for a comprehensively understanding of the essential features. Specifically, this paper develops the learnable parallel convolutional-Transformer mixing module to parallelly capture the sophisticated local features as well as the crucial global context. In addition, a new fusion unit is devised in the paper and deployed in the learnable parallel convolutional-Transformer mixing module. The proposed fusion unit is capable of effectively mixing contextual features extracted at both global and local scales while retaining an abundant level of textural details germane to the crack. Moreover, this paper constructs a graph-based skip connection that functions as a shortcut connecting the encoder and decoder, with the primary objective of mitigating information decay. The experimental results are remarkable, with the Mix-Graph CrackNet achieving F-measure and Intersection-Over-Union of 90.37% and 82.43%, respectively, on 1000 testing images. Based on the performance evaluations conducted on both public and private datasets, the proposed Mix-Graph CrackNet architecture demonstrates a significantly superior detection accuracy in comparison to several state-of-the-art models for semantic segmentation.

Skid resistance is an important surface characteristic that influences roadway safety. Various studies have been performed to understand the interaction between pavement and tires through numerical simulation for skid resistance prediction. However, the friction parameters required for simulation inputs are generally determined by objective assumptions. This paper develops a finite element method (FEM)-based skid resistance simulation framework using in-situ 3D pavement surface texture and skid resistance data. A 3D areal pavement model is reconstructed from high resolution asphalt pavement surface texture data. The exponential decay friction model is implemented in the simulation and the interface friction parameters required for the simulation are determined using the binary search back-calculation approach based on a trial process with the desired level of differences between simulated and observed skid numbers. To understand the influence of texture characteristics on interface friction parameters, the high-resolution 3D texture data is separated into macro- and micro-scales through Butterworth filtering and various areal texture indicators are calculated at both levels. Principal component analysis (PCA) regression analysis is conducted to quantify the relationship between various texture characteristics and the interface friction parameters. The results from this study can be used to better prepare the inputs of friction parameters for FEM simulation.

This study develops a three-dimensional (3D) Laser Railway Detection System for automated railway fastener defect detection on 3D ballastless track. The 3D laser imaging system overcomes the shortcomings of shadows and illumination variations, thereby providing 3D information of the ballastless track with high reproducibility and accuracy. RailNet, an efficient architecture based on a Convolutional Neural Network (CNN), is proposed in this paper for detecting high-speed railway fastener defects on 3D ballastless track. RailNet consists of 10 layers and includes more than 120,000 parameters. RailNet is trained using 80,000 3D fastener images with 1-mm resolution and is then demonstrated to be successful at identifying damaged and missing fasteners. The testing results show that the system described in this paper can inspect the defective hook-shaped fasteners notably well. The proposed RailNet significantly outperforms the other approaches with a prediction accuracy of 100%, and the number of testing samples is 16,000.

Master curve technique is frequently used to characterize the linear viscoelasticity of polymer modified asphalt (PMA), however, there is no widely accepted master curve for PMA. This study aims to propose a novel double sigmoidal model that can be used universally for all asphalts, especially highly modified PMAs. To do so, one plain asphalt and six representative PMAs are examined. Extended temperature sweep tests (−40°C to 140°C) are conducted to grasp the whole picture of the master curve. Three representative mathematical models (CAM, sigmoidal, double sigmoidal) are selected and compared in terms of fitting in both modulus and phase angle. The results suggest that double sigmoidal model suits all types of asphalts for all testing temperature ranges, and it features the ability to model the rubbery state and rubbery-viscous transition of highly modified PMA (RMSE < 0.02). A phase angle expression of the double sigmoidal model is also developed, which shows great potential in characterizing the strength of polymer network within the PMA. Also, it avoids the impact from the partial break of the time-temperature superposition principle (TTSP) and reveals the true shape of the phase angle master curve. Finally, master curve models are recommended based on different testing temperature ranges.