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It is widely recognized that pavement surface texture is important for pavement friction and roadway safety. Mean profile depth, the primary index used to characterize macrotexture, is a two-dimensional height indicator calculated from a single profile, which is insufficient to represent surface texture, especially for friction studies. This paper explores five categories of three-dimensional (3-D) areal parameters to characterize pavement texture attributes and develops the relationship between pavement friction and the 3-D texture parameters. The newly constructed Long-Term Pavement Performance Specific Pavement Study 10 site in Oklahoma was selected as the test bed. Pavement texture and friction data were collected in parallel at the same predefined locations with a portable ultrahigh-resolution 3-D laser scanner and a dynamic friction tester, respectively. Correlation analyses among the 24 3-D texture parameters were conducted to exclude those that exhibited strong correlations and to remove the potential multicollinearity for regressional friction model development. Multivariate analysis was then performed to develop the relationship between the selected 3-D texture parameters and dynamic friction tester friction data at various testing speeds. Core material volume and peak density were identified as the most influential macro- and microtexture parameters that exhibit fairly good correlation with friction data at high and low speeds in wet conditions. Results indicate the identified 3-D texture parameters provide better alternatives for characterizing pavement surface texture attributes with respect to pavement friction performance.

Simultaneous pixel-level detection of multiple distresses and surface design features on complex asphalt pavements is a critical challenge in intelligent pavement survey. This paper proposes a deep-learning model named ShuttleNet to provide an efficient solution for this challenge by implementing robust semantic segmentation on asphalt pavements. The proposed ShuttleNet aims at repeating the encoding-decoding round freely or even endlessly such that the contexts at different resolution levels can be learned and integrated many times for enhanced latent representations. Additionally, a new and efficient connection method called memory connection is also proposed in the paper and deployed in the ShuttleNet model to provide shortcut connections between successive encoding-decoding rounds. The proposed memory connection can partially or entirely carry the decoded information at different resolution levels into the next encoding-decoding round. Pairing 3D pavement images with 2D pavement images, the proposed ShuttleNet model is applied to detect multiple distresses and surface design features on asphalt pavements simultaneously, including pavement cracks, potholes, sealed cracks, patches, markings, expansion joints, and the pavement background. Experimental results demonstrate that the mean F-measure and mean intersection-over-union attained by the recommended architectural variation of the proposed ShuttleNet model on 1500 testing image pairs are 92.54% and 0.8657 respectively. According to the performance comparisons using both private and public datasets, the proposed ShuttleNet model can yield a noticeably higher detection accuracy, compared with four 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.