Innovation for evaluating aggregate angularity based upon 3D convolutional neural network

The performance of asphalt pavement is significantly influenced by the morphological characteristics of its aggregates, especially on its angularity. Evaluation of aggregate angularity is considered to be challenging because aggregates often have various shapes. Therefore, utilization of digital images in the evaluation of angularity has gained significant research interest in recent years. However, conventional manually processed images for evaluating the angularity of aggregates have the disadvantages of low efficiency and insufficient accuracy. This paper presents a novel application of convolutional neural networks (CNN) using digital images for evaluating aggregate angularity automatically. The research procedure is as follows: (a) develop a self-developed device for the acquisition of aggregate images; (b) establish an evaluation criterion for the angularity index (AI); (c) design a localization CNN and five AI CNNs; and (d) conduct a sensitivity analysis of the CNNs. First, a self-developed device is established based on the view-based approach to extract the 3D information of aggregates. Then, an evaluation criterion that is suitable for 3D images from aggregates is presented. Based on the 3D images and evaluation criterion, one localization CNN and five AI CNNs are jointly used to evaluate the AI of each aggregate. Finally, statistical analysis is performed to seek the optimal parameters for AI CNN, especially the kernel size, and to verify the sensitivity of AI CNN. The analysis includes the sensitivity to kernels size, image resolution, light, texture and aggregate size. The results indicate that the localization CNN is able to locate and abstract each aggregate from the images. The best size of the kernels is 6 × 6, and an AI CNN with a kernel size of 6 × 6 has a 0.0938 relative error for evaluating the AI using 300 PPI images. Moreover, AI CNN with a kernel size of 6 × 6 shows remarkable robustness under different light conditions, sizes and textures of aggregates.