Sparse representation for damage identification of structural systems

• URL: http://arxiv.org/abs/2006.03929v1
• Date: Sat, 6 Jun 2020 18:04:35 GMT
• Title: Sparse representation for damage identification of structural systems
• Authors: Zhao Chen and Hao Sun
• Abstract summary: We propose a novel two-stage sensitivity analysis-based framework for both model updating and sparse damage identification. A sparse representation pipeline built on a quasi-$ell$ method is then presented for damage and localization quantification. Results show that the proposed approach is capable of both localizing and quantifying structural damage with high accuracy.
• Score: 11.397437423613418
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Identifying damage of structural systems is typically characterized as an inverse problem which might be ill-conditioned due to aleatory and epistemic uncertainties induced by measurement noise and modeling error. Sparse representation can be used to perform inverse analysis for the case of sparse damage. In this paper, we propose a novel two-stage sensitivity analysis-based framework for both model updating and sparse damage identification. Specifically, an $\ell_2$ Bayesian learning method is firstly developed for updating the intact model and uncertainty quantification so as to set forward a baseline for damage detection. A sparse representation pipeline built on a quasi-$\ell_0$ method, e.g., Sequential Threshold Least Squares (STLS) regression, is then presented for damage localization and quantification. Additionally, Bayesian optimization together with cross validation is developed to heuristically learn hyperparameters from data, which saves the computational cost of hyperparameter tuning and produces more reliable identification result. The proposed framework is verified by three examples, including a 10-story shear-type building, a complex truss structure, and a shake table test of an eight-story steel frame. Results show that the proposed approach is capable of both localizing and quantifying structural damage with high accuracy.

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