Vision-aided three-dimensional damage quantification and finite element model geometric updating for reinforced concrete structures

This article presents a vision-aided framework to achieve three-dimensional (3D) concrete damage quantification and finite element (FE) model geometric updating for reinforced concrete structures. The framework can process images and point clouds to extract damage information and update it in an FE model. First, a mask region convolutional neural network was used to realize highly precise damage detection and segmentation based on images. Second, a 3D point cloud was adopted in conjunction with the processed images for 3D damage qualification. The model-updating method enables an FE model to delete concrete elements to update the variations in volume caused by structural damage. This framework supports interaction with mainstream FE software for further analysis. To demonstrate the efficiency of the proposed framework, it was used in an experiment on a reinforced-concrete shear wall.