Localization and classification of structural damage using deep learning single-channel signal-based measurement

Abstract

Diligent damage identification is a core thrust of structural health monitoring (SHM). Vibration-based SHM has recently gained paramount importance. Substantial research devoted to Deep Learning (DL) algorithms yielded superior accuracy. This study proposes a novel DL-based damage detection approach to automatically extract features from raw acceleration sensor data. A new One-Dimensional Convolutional Neural Network named BuildingNet was designed to learn features and identify damage locations in real-time under different damage assessment scenarios. Parametric studies were conducted on different layer numbers, size of training datasets, and noise levels. Systematic studies for optimizing the network architecture and training data were performed. Mid-rise building case studies demonstrated the accuracy and efficiency of the proposed model. Time-domain monitoring data, both from multiple and single-channel measurements, were used for training and testing different architectures of BuildingNet. The proposed model achieved excellent damage localization and classification performance on both noise-free and noisy data sets.