Gordan, Meisam and Razak, Hashim Abdul and Ismail, Zubaidah and Ghaedi, Khaled (2018) Data mining based damage identification using imperialist competitive algorithm and artificial neural network. Latin American Journal of Solids and Structures, 15 (8). e107. ISSN 1679-7817, DOI https://doi.org/10.1590/1679-78254546.
Full text not available from this repository.Abstract
Currently, visual inspections for damage identification of structures are broadly used. However, they have two main drawbacks; time limitation and qualified manpower accessibility. Therefore, more precise and quicker technique is required to monitor the condition of structures. To aid the aim, a data mining based damage identification approach can be utilized to solve these drawbacks. In this study, to predict the damage severity of sin-gle-point damage scenarios of I-beam structures a data mining based damage identification framework and a hybrid algorithm combining Artificial Neural Network (ANN) and Imperial Competitive Algorithm (ICA), called ICA-ANN method, is proposed. ICA is employed to determine the initial weights of ANN. The efficiency coefficient and mean square error (MSE) are used to evaluate the performance of the ICA-ANN model. Moreover, the proposed model is compared with a pre-developed ANN approach in order to verify the efficiency of the proposed methodology. Based on the obtained results, it is concluded that the ICA-ANN indicates a better performance in detection of damage severity over the ANN method used only.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Structural health monitoring; damage detection; data mining; artificial neural network; imperial competitive algorithm; hybrid algorithm |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 20 Aug 2019 09:16 |
| Last Modified: | 20 Aug 2019 09:16 |
| URI: | http://eprints.um.edu.my/id/eprint/22002 |
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