Damage identification using noisy frequency response functions based on topology optimization
Akira Saito, Ryo Sugai, Zhongxu Wang, Hidetaka Saomoto

TL;DR
This paper introduces a robust damage identification technique using noisy frequency response functions and topology optimization, effectively detecting damage regions in structures despite measurement noise.
Contribution
It develops a novel inverse problem formulation with Lasso regularization to improve damage detection accuracy under noisy conditions.
Findings
Successfully identified damage in cantilevered plates
Lasso regularization reduces spurious damage regions
Method is robust against measurement noise
Abstract
This paper proposes a robust damage identification method using noisy frequency response functions (FRFs) and topology optimization. We formulate the damage identification problem as an inverse problem of generating the damage topology of the structure from measured dynamic responses of the structure to given external dynamic loading. The method is based on the minimization of the objective function representing errors between measured FRFs of the structure obtained by experimental modal analysis, and those obtained by harmonic response analysis using finite element analysis. In the minimization process, material distribution, or the topology of the structure is varied and the optimal damage topology is identified as regions with no material assigned as a result of the minimization using the solid isotropic material with penalization (SIMP). In order to overcome the problems caused by…
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Taxonomy
TopicsTopology Optimization in Engineering · Structural Health Monitoring Techniques · Aeroelasticity and Vibration Control
