Utilizing mode shape curvature and artificial neural networks for structural damage assessment in beam-like structures
Damage within a structure refers to changes in both its geometric and material characteristics, resulting in a drop in the stiffness that impacts the structure's performance adversely. This decrease in stiffness causes alterations in modal parameters, including natural frequencies and mode s...
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| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | en |
| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/12593/1/P17983_5357c1ff0f698a8d4cda11ad289e0e74.pdf http://eprints.uthm.edu.my/12593/ https://iopscience.iop.org/article/10.1088/1755-1315/1453/1/012013 |
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| Summary: | Damage within a structure refers to changes in both its geometric and material
characteristics, resulting in a drop in the stiffness that impacts the structure's performance
adversely. This decrease in stiffness causes alterations in modal parameters, including natural
frequencies and mode shapes. Utilizing modal analysis allows for the extraction of modal
frequencies and mode shapes, facilitating the analysis of mode shape curvature to detect
structural damage. In recent years, artificial neural networks (ANNs) have achieved significant
application, mainly for their exceptional capability in pattern recognition, which proves
invaluable for identifying structural damage. This article proposes a novel method based on
mode shape curvature and ANNs for detecting damage in beam-like structures. Experimental
study is conducted to analysis damaged and undamaged structural modal behaviours. A feed-
forward neural network with two hidden layers, trained on damage indices from mode shape
data, is used to accurately pinpoint damage locations within the structure. The proposed approach
for damage detection is validated and proves its ability to precisely pinpoint the location of
damage. The results of this study demonstrate that ANNs trained with modal curvatures hold
significant promise for identifying structural damage, enabling early detection in beam-like
structures and contributing to ensuring their safe operation. |
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