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,
Full text not available from this repository.Abstract
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.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Additional Information: | 224EX Times Cited:1 Cited References Count:29 |
| Uncontrolled Keywords: | Artificial neural networks (ANNs), Backpropagation algorithm, Damage detection, Finite element, Natural frequency, Damage Identification, Finite element models, Natural frequencies and modes, Steel bridge girders, Steel girder bridge, Structural damage detection, Structural damage identification, Backpropagation algorithms, Finite element method, Modal analysis, Neural networks, Plate girder bridges, Natural frequencies |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering |
| Depositing User: | Mr Jenal S |
| Date Deposited: | 24 Jan 2014 14:53 |
| Last Modified: | 29 Oct 2014 06:43 |
| URI: | http://eprints.um.edu.my/id/eprint/9053 |
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