Feature Selection Using Tree Model and Classification Through Convolutional Neural Network for Structural Damage Detection

Abstract

Structural damage detection (SDD) remains highly challenging, due to the difficulty in selecting the optimal damage features from a vast amount of information. In this study, a tree model-based method using decision tree and random forest was employed for feature selection of vibration response signals in SDD. Signal datasets were obtained by numerical experiments and vibration experiments, respectively. Dataset features extracted using this method were input into a convolutional neural network to determine the location of structural damage. Results indicated a 5% to 10% improvement in detection accuracy compared to using original datasets without feature selection, demonstrating the feasibility of this method. The proposed method, based on tree model and classification, addresses the issue of extracting effective information from numerous vibration response signals in structural health monitoring.