Damage detection in slab structures based on two-dimensional curvature mode shape method and Faster R-CNN

Abstract

This paper proposes a novel method based on the two-dimensional (2D) curvature mode shape method, Convolutional Neural Networks (CNN), and Faster Region-based Convolutional Neural Networks (faster R-CNN) for detecting damage in slab structures. The 2D curvature mode shapes could be measured directly or calculated from the measured mode shapes using the central difference method. The damage indicator is defined as the absolute differences between the 2D curvature mode shapes of the damaged and intact slabs. The contour plot is chosen to convert the damage indicators into images. Four hundred damage scenarios are created using a Finite Element (FE) model of the slab. Images created from those damage scenarios are then used to train CNN and faster R-CNN. Four damage types are considered in this research, namely a single small hole, a single big hole, two small holes, and two large holes. After training, CNN can predict the damage types and faster R-CNN can predict the bounding boxes around the damaged areas. A test sample set is created to test the performance of the proposed method. The effect of noise in the mode shape data is considered. Results show that the classification accuracy for damage type is high. The overlap ratios between the predicted bounding boxes and the real damaged areas are more than 40% for 80% of tested scenarios. Furthermore, the low influence of noise on the predicted results is investigated. The proposed method is robust and has great potential for application to real structures.