Advanced Damage Monitoring in Beam Structures Using Grey Wolf Optimizer and Additional Masses
Dynamic characteristics are of significant interest to researchers in the field of damage detection. Among these, natural frequencies stand out due to their high accuracy and resistance to noise. However, relying solely on natural frequencies is often insufficient for determining the depth and locat...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/15/2/279 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Dynamic characteristics are of significant interest to researchers in the field of damage detection. Among these, natural frequencies stand out due to their high accuracy and resistance to noise. However, relying solely on natural frequencies is often insufficient for determining the depth and location of damage. To address this limitation, additional masses can be strategically placed at different locations on structural elements, altering the natural frequencies. Each mass placement creates a distinct dynamic scenario with a unique frequency profile, enabling a more comprehensive analysis. In this study, additional masses were introduced at specific elements of the beam structure within the numerical model which were then strategically placed at various locations along the beam. The resulting shifts in natural frequencies served as inputs to the Grey Wolf Optimizer (GWO), which identified elements with stiffness reductions indicative of damage. A custom MATLAB code was developed to perform finite element analysis on the numerical model. The results were validated against previously published experimental data, demonstrating the method’s reliability with a 5% difference. A parametric study involving both simple and continuous span beams was performed. The procedure effectively detected damage severities of 10%, 25%, and 50%, with corresponding errors of 4.3%, 0.44%, and 0.02%, respectively. |
|---|---|
| ISSN: | 2075-5309 |