Seismic damage identification based on integrated artificial neural networks and wavelet transforms

Abstract

In recent years, Structural Health Monitoring (SHM) has been proposed and practiced for condition assessment of structures. SHM covers shortcomings of nondestructive tests and is comprised of a sensory system, data acquisition system, and damage identification system. In this study, numerical and experimental investigations are concentrated on the application of Artificial Neural Networks (ANNs) and Wavelet Transforms (WTs) for damage identification of civil engineering structures. As a major outcome of this research, three novel damage identification methods are developed. The first damage identification method enables the SHM systems to identify damage to cantilever structures through decomposition of mode shapes by integrating WTs and ANNs. The second damage identification method enables SHM systems to identify damage to cantilever structures via decomposition of response accelerations by means of WTs and ANNs. The third damage identification method takes advantage of only ANNs and enables the SHM systems to identify seismic-induced damage to concrete shear walls in real-time by measuring inter-storey drifts. In addition, a novel optimal strain gauge placement method for seismic health monitoring of structures is proposed. This method considers the seismicity of construction site and the importance level of structures. Results from the first method showed that when the imposed damage levels were severe, medium, and light, the proposed method could quantify them with less than 5%, 12%, and 16% errors, respectively. In addition, the second method quantified seismic-induced damage to the studied structure with an averaged error of 8%. Moreover, the third method classified damage levels of the studied concrete shear walls with a success rate of 91%. The proposed optimal strain gauge placement method reduced the number of required sensors for the studied structure from 206 sensors to 73 sensors. The obtained results demonstrated the feasibility, robustness, and efficiency of the proposed methods for damage identification of civil engineering structures.