Structural damage identification using inverse analysis through optimization with sparsity

Yang Zhang; K. Zhou; J. Tang

doi:10.1117/12.2613400

18 April 2022 Structural damage identification using inverse analysis through optimization with sparsity

Yang Zhang, K. Zhou, J. Tang

Proceedings Volume 12046, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2022; 1204606 (2022) https://doi.org/10.1117/12.2613400
Event: SPIE Smart Structures + Nondestructive Evaluation, 2022, Long Beach, California, United States

Abstract

Structural damage identification using the impedance/admittance measurements of a piezoelectric transducer can be converted into a multi-objective optimization framework targeting the minimization of the discrepancy between prediction and experimental measurements, with damage locations and severities as unknown variables. However, the unknowns are usually on a large scale and show sparse characteristics since the damage only occurs at a small area. This places the burden on the optimization algorithms in the identification process. Here, a sparse initialization algorithm is introduced to generate a sparse population for the large-scale variables to tackle the challenge. The algorithm is combined with the particle swarm algorithm to locate and quantify the damage for verification purposes. Several cases are considered, and the results show that the algorithm can generate high-quality damage identification solutions with limited simulation and experimental measurements.

Conference Presentation

Citation Download Citation

Yang Zhang, K. Zhou, and J. Tang "Structural damage identification using inverse analysis through optimization with sparsity", Proc. SPIE 12046, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2022, 1204606 (18 April 2022); https://doi.org/10.1117/12.2613400

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