@inproceedings{13ed65495b3742528dee6c5d8dfed699,
title = "Structural Damage Identification using Inverse Analysis through Optimization with Sparsity",
abstract = "Structural damage identification using piezoelectric impedance/admittance measurements of a piezoelectric transducer can be converted into an optimization problem that minimizes the difference between experimental measurements and prediction in the parametric space where damage locations and severities are treated as unknown variables. However, the number of unknowns is large. Meanwhile, in practical situations the location of damage occurrence is usually limited. In this research, we propose a multi-objective particle swarm optimization algorithm featuring a sparse population generation enhancement to tackle the challenge. The main idea is to design a masking procedure, so the damage location identified is sparse that fits the nature of damage identification. This approach is implemented to experimental testing for demonstration and validation.",
keywords = "damage identification, multi-objective optimization, piezoelectric transducer, sparsity",
author = "Yang Zhang and K. Zhou and J. Tang",
note = "Funding Information: This research is supported in part by NSF under grant CMMI – 1825324 and in part by the Infrastructure Durability Center at the University of Maine under grant 69A3551847101. Publisher Copyright: {\textcopyright} 2022 SPIE.; Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2022 ; Conference date: 04-04-2022 Through 10-04-2022",
year = "2022",
month = apr,
doi = "10.1117/12.2613400",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Daniele Zonta and Daniele Zonta and Branko Glisic and Zhongqing Su",
booktitle = "Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2022",
address = "United States",
}