Structural damage identification using inverse analysis through optimization with sparsity

Document Type

Conference Proceeding

Publication Date



Department of Mechanical Engineering-Engineering Mechanics


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.

Publication Title

Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2022