Structural damage detection of steel bridge girder using artificial neural networks and finite element models
Damage in structures often leads to failure. Thus it is very important to monitor structures for the occurrence of damage. When damage happens in a structure the consequence is a change in its modal parameters such as natural frequencies and mode shapes. Artificial Neural Networks (ANNs) are inspire...
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| Format: | Article |
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2013
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| author | Hakim, S.J.S. Abdul Razak, H. |
| author_facet | Hakim, S.J.S. Abdul Razak, H. |
| author_sort | Hakim, S.J.S. |
| collection | UM |
| description | Damage in structures often leads to failure. Thus it is very important to monitor structures for the occurrence of damage. When damage happens in a structure the consequence is a change in its modal parameters such as natural frequencies and mode shapes. Artificial Neural Networks (ANNs) are inspired by human biological neurons and have been applied for damage identification with varied success. Natural frequencies of a structure have a strong effect on damage and are applied as effective input parameters used to train the ANN in this study. The applicability of ANNs as a powerful tool for predicting the severity of damage in a model steel girder bridge is examined in this study. The data required for the ANNs which are in the form of natural frequencies were obtained from numerical modal analysis. By incorporating the training data, ANNs are capable of producing outputs in terms of damage severity using the first five natural frequencies. It has been demonstrated that an ANN trained only with natural frequency data can determine the severity of damage with a 6.8 error. The results shows that ANNs trained with numerically obtained samples have a strong potential for structural damage identification. Copyright © 2013 Techno Press. |
| first_indexed | 2024-03-06T05:22:48Z |
| format | Article |
| id | um.eprints-9053 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:22:48Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | um.eprints-90532014-10-29T06:43:31Z http://eprints.um.edu.my/9053/ Structural damage detection of steel bridge girder using artificial neural networks and finite element models Hakim, S.J.S. Abdul Razak, H. TA Engineering (General). Civil engineering (General) Damage in structures often leads to failure. Thus it is very important to monitor structures for the occurrence of damage. When damage happens in a structure the consequence is a change in its modal parameters such as natural frequencies and mode shapes. Artificial Neural Networks (ANNs) are inspired by human biological neurons and have been applied for damage identification with varied success. Natural frequencies of a structure have a strong effect on damage and are applied as effective input parameters used to train the ANN in this study. The applicability of ANNs as a powerful tool for predicting the severity of damage in a model steel girder bridge is examined in this study. The data required for the ANNs which are in the form of natural frequencies were obtained from numerical modal analysis. By incorporating the training data, ANNs are capable of producing outputs in terms of damage severity using the first five natural frequencies. It has been demonstrated that an ANN trained only with natural frequency data can determine the severity of damage with a 6.8 error. The results shows that ANNs trained with numerically obtained samples have a strong potential for structural damage identification. Copyright © 2013 Techno Press. 2013 Article PeerReviewed Hakim, S.J.S. and Abdul Razak, H. (2013) Structural damage detection of steel bridge girder using artificial neural networks and finite element models. Steel and Composite Structures, 14 (4). pp. 367-377. ISSN 1229-9367, http://www.scopus.com/inward/record.url?eid=2-s2.0-84876835623&partnerID=40&md5=d8f8ef7ab73f99a132d0b42dad1028ec http://www.koreascience.or.kr/article/ArticleFullRecord.jsp?cn=KJKHEW₂₀₁₃v14n4₃₆₇ |
| spellingShingle | TA Engineering (General). Civil engineering (General) Hakim, S.J.S. Abdul Razak, H. Structural damage detection of steel bridge girder using artificial neural networks and finite element models |
| title | Structural damage detection of steel bridge girder using artificial neural networks and finite element models |
| title_full | Structural damage detection of steel bridge girder using artificial neural networks and finite element models |
| title_fullStr | Structural damage detection of steel bridge girder using artificial neural networks and finite element models |
| title_full_unstemmed | Structural damage detection of steel bridge girder using artificial neural networks and finite element models |
| title_short | Structural damage detection of steel bridge girder using artificial neural networks and finite element models |
| title_sort | structural damage detection of steel bridge girder using artificial neural networks and finite element models |
| topic | TA Engineering (General). Civil engineering (General) |
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