Published: 2022

Publication Name: Structural damage identification using inverse analysis through optimization with sparsity

Publication URL: https://doi.org/10.1117/12.2613400

Abstract:

TIDC Projects 1.06 & 1.16

 

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.