Modal parameter extraction from improved principal component analysis and structural state identification

Abstract

With the vigorous development of building structures and important infrastructure, structural health monitoring is necessary. Because there is no need to establish structural finite element modeling and train for various structural conditions, the data-driven and unsupervised learning method is very popular. Principal component analysis is a powerful signal analysis tool, but its lack of physical significance and the loss of sensitive information have hindered its wider application. Therefore, the improved principal component analysis based narrowband filtering is proposed to extract mode shapes and construct the structural state vectors, so that the damage index is more sensitive to damage and robust to the environmental factors. After the vibration response of the long-term monitoring is analyzed by the principal component analysis, the Gaussian mixture model clustering analysis is used to classify the structural states. Finally, the proposed method is applied to the analysis of the simulation data of ASCE Benchmark structure and the measured data of steel beams in the lab. The results show that the structural state vector is sensitive to structural damage. The clustering analysis of Gaussian mixture model can distinguish the structural states. The effectiveness of the proposed method is verified.