2020/06/06 by Zhao Chen, Hao Sun, Chen, Zhao +1
Computer Science · Decision Sciences · Engineering · Mathematics · Physics and Astronomy · #Applications (stat.AP) #Data Analysis #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #FOS: Physical sciences #Machine Learning (cs.LG) #Numerical Analysis (math.NA) #Probabilistic and Robust Engineering Design #Statistics and Probability (physics.data-an) #Structural Health Monitoring Techniques #Systems and Control (eess.SY) #Ultrasonics and Acoustic Wave Propagation #cs.LG #cs.NA #cs.SY #eess.SY #electronic engineering #information engineering #math.NA #physics.data-an #stat.AP
paper · pdf · doi:10.48550/arxiv.2006.03929
11 pages, 11 figures
arxiv created 2020/06/06 · openalex publication_date 2020/06/06 · arxiv updated 2020/06/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 ℓ2 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-ℓ0 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.