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Vibration-Based Damage Detection Using Finite Element Modeling and the Metaheuristic Particle Swarm Optimization

Ilias Zacharakis¹, Dimitrios Giagopoulos¹

¹Department of Mechanical Engineering, University of Western Macedonia, Bakola and Sialvera, 50100 Kozani, Greece.

Sensors (Basel, Switzerland)

|July 27, 2022

Summary

A new vibration-based Structural Health Monitoring (SHM) method uses metaheuristic algorithms and Finite Element Models (FEM) to accurately detect and localize damage in structures. This approach enhances structural safety by pinpointing specific damaged areas within components.

Keywords:

FE model updating damage detection damage localization metaheuristic algorithms model-based vibration-based

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Area of Science:

Structural Engineering
Materials Science
Computational Mechanics

Background:

Continuous development of complex structures necessitates advanced Structural Health Monitoring (SHM) techniques.
Existing SHM methods require improvement in robustness, accuracy, and sensitivity for damage detection and localization.
Vibration-based methods offer a promising avenue for non-destructive damage assessment.

Purpose of the Study:

To present a novel vibration-based damage-detection method for SHM.
To effectively localize damage in structures using metaheuristic algorithms and optimal Finite Element Models (FEM).
To develop a framework applicable to detailed structural FEMs using only dynamic response data.

Main Methods:

Development of an optimal FEM for the healthy structure using model updating techniques and experimental data.
Creation of a damaged FEM by inserting a parametric area to simulate damage effects (stiffness and mass reduction).
Utilization of the Particle Swarm Optimization (PSO) algorithm to control damage simulation parameters and location within the FEM.
Application of Transmittance Functions from acceleration measurements for damage localization using output-only information.

Main Results:

The proposed framework successfully localized damage in both a simulated vehicle-like structure and a real CFRP composite beam.
The method demonstrated robustness by examining two damage scenarios under random excitations for each validation model.
Accurate prediction of damaged locations was achieved by minimizing modeling errors through objective function selection.

Conclusions:

The developed SHM method effectively localizes structural damage by integrating metaheuristic optimization with FEM.
The approach provides a sensitive and accurate tool for identifying not only the affected part but also the specific damaged area.
This novel technique contributes significantly to advancing SHM capabilities for complex engineering structures.