Damage detection of steel bridge girder using Artificial Neural Networks
Civil structures are exposed to damage during their service life which can severely affect their safety and functionality. Thus it is important to monitor structures for the occurrence, location and extent of damage. Artificial Neural Networks (ANNs) are inspired by human biological neurons and have...
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| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Published: |
2012
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| Subjects: | |
| Online Access: | http://eprints.um.edu.my/9052/ http://www.scopus.com/inward/record.url?eid=2-s2.0-84856646353&partnerID=40&md5=e0c18c42ab0d418fed53927e5a17854c http://www.crcnetbase.com/doi/pdfplus/10.1201/b11837-74 |
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| Summary: | Civil structures are exposed to damage during their service life which can severely affect their safety and functionality. Thus it is important to monitor structures for the occurrence, location and extent of damage. Artificial Neural Networks (ANNs) are inspired by human biological neurons and have been applied dramatically for damage identification with varied success. The feasibility of ANNs as strong tool for predicting the severity of damage in a model steel girder bridge is examined in this research. Natural frequencies of a structure have strong effect on damage and are applied as effective input parameters to train the ANN in present study. The required data for the ANNs in the form of natural frequencies are obtained from experimental modal analysis. It has been shown that an ANN trained only with natural frequency data can determine the severity of damage with less than 5.6 error. The results seem to be quite promising as accurately as possible. © 2012 Taylor & Francis Group, London. |
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