A hierarchical neural network for identification of multiple damage using modal parameters
Artificial neural networks have been applied extensively in recent years due to their excellent performance in pattern recognition, which is useful for detecting damage in structural elements. The application of multiple damage cases by an ensemble neural network using dynamic parameters of struct...
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| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/11117/1/P16682_74e0e3ab01b75e48402da271f894d8d4%209.pdf http://eprints.uthm.edu.my/11117/ https://doi.org/10.1063/5.0149295 |
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| Summary: | Artificial neural networks have been applied extensively in recent years due to their excellent performance in
pattern recognition, which is useful for detecting damage in structural elements. The application of multiple damage cases
by an ensemble neural network using dynamic parameters of structure is very limited. Therefore, in this paper, an ensemble
neural network based on damage identification techniques was developed and applied for damage localization and severity
identification of quad-point damage cases in I-beam structure. Experimental modal analysis and finite element simulation
were carried out for I-beam with four-point damage cases to generate the modal parameters of the structure. Based on the
results, it is found that the ensemble neural networks achieve a high detecting accuracy and good robustness of quad-point
damage cases in I-beam structures |
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