Artificial Intelligence-Based Damage Identification Method Using Principal Component Analysis with Spatial and Multi-Scale Temporal Windows

Abstract

Previous studies have demonstrated the superior damage identification performance of the double-window principal component analysis (DWPCA) method over traditional PCA methods and other traditional techniques, such as wavelet and regression analysis. DWPCA uses temporal windows to discriminate structural states and spatial windows to exclude damage-insensitive responses, making it more effective for damage identification. However, determining the optimal temporal window scale and its impact on damage identification performance still remains unclear. In this study, different scales of temporal windows, including yearly, seasonal and monthly windows, are employed to obtain corresponding damage features, i.e., eigenvectors derived from DWPCA. These damage-sensitive eigenvectors from various temporal windows are then used as inputs for artificial intelligence (AI) algorithms to localize and quantify damages. In this paper two types of AI algorithms are employed: random forest (RF) and bidirectional gated recurrent unit (BiGRU). A numerical study using a benchmark model is used to evaluate the contribution of the eigenvector of each temporal scale to damage identification. The results demonstrate that the combined DWPCA eigenvectors [Formula: see text] from the three temporal windows effectively enhance the AI-based damage identification capability. Besides, AI algorithm with [Formula: see text] can have high accuracy exceeding 95% under limited training data sets and strong noise. Additionally, when DWPCA eigenvectors from monthly or seasonal windows as inputs, which is both sensitive to damages and noise, the BiGRU also achieves high accuracy of over 90% for damage identification, due to its advantages in feature extraction. These findings suggest that the proposed approach has significant potential for real-life structural health monitoring applications.