Point cloud-based dimensional quality assessment of precast concrete components using deep learning

Abstract

The dimensional quality of precast concrete (PC) subcomponents (concrete and rebars) should be inspected in advance to ensure assembly quality. Currently, PC components are mainly inspected in a manual manner using tools such as tape measure, which is error-prone and inefficient. This study developed an innovative approach for automatic dimensional quality assessment of PC components using point cloud-based deep learning techniques. The approach consists of 1) a dataset-generating method to automatically create the synthetic dataset of PC components’ point clouds, 2) an enhanced focal loss-based precast concrete component recognition net (PCCR-Net) employing hierarchical feature learning to segment the synthetic point clouds dataset into rebars and concrete (i.e. the synthetic dataset generated is used to train the PCCR-Net), and 3) a quantitative measurement protocol that can estimate the dimensional quality of the segmented concrete and rebars. Experiments were conducted to test the capability of the approach, and the results show that the proposed approach was able to yield satisfactory performance. First, the dataset-generating method can solve the shortage of point cloud datasets in engineering practice. Second, the PCCR-Net segmentation network can simultaneously realize the high-precision identification of various typical PC components, including PC columns, beams, slabs, and walls. Third, the dimension average deviations between experimental results and manual measurements demonstrate that the assessment approach can accurately estimate the dimensions of PC components.