Damage Identification Using Nonlinear Manifold Learning Method under Changing Environments

Abstract

Damage identification is a key aspect of structural health monitoring (SHM). However, any measurement of the structural response can be impacted by environmental and operational variations (EOVs), which can affect the system and hinder damage detection. It is therefore important to distinguish between damage-induced changes in structural dynamic properties and changes caused by EOVs. To address this issue, this paper proposes a damage identification method based on nonlinear manifold learning, specifically Laplacian eigenmaps (LEs). The method eliminates the impact of EOVs on the damage index by treating them as embedded variables and does not require the direct measurement of environmental parameters. The Gaussian process regression (GPR) prediction model results in small residuals when the structure is healthy and significant increases when the structure is damaged, demonstrating the effectiveness of the method in removing environmental influences. The proposed method is demonstrated using computer-simulated data, where the environmental conditions have a nonlinear effect on the vibration features. The proposed LE-GPR algorithm is then applied to the Z24 and KW51 bridges and successfully identifies structural damage. The advantage of the proposed approach is its ability to eliminate the effects of ambient temperature and accurately identify structural damage.