Gordan, Meisam and Ismail, Zubaidah and Razak, Hashim Abdul and Ghaedi, Khaled and Ibrahim, Zainah and Tan, Zhi Xin and Ghayeb, Haider Hamad (2020) Data mining-based damage identification of a slab-on-girder bridge using inverse analysis. Measurement, 151. ISSN 0263-2241, DOI https://doi.org/10.1016/j.measurement.2019.107175.
Full text not available from this repository.Abstract
Classical damage detection methods such as visual inspections have many limitations, i.e. time consuming procedure, costly process and ineffective for large and complex structural systems. To overcome these difficulties, a data mining-based damage identification approach is developed in this study. First four natural frequencies which obtained from the experimental modal analysis of a slab-on-girder bridge structure are used as an input database. The laboratory work is carried out through single-type and multiple-type damage scenarios. The applicability of machine learning, artificial intelligence and statistical data mining techniques are here examined using Support Vector Machine (SVM), Artificial Neural Network (ANN) and Classification and Regression Tree (CART) to predict the model behavior and damage severity. Then, a hybrid algorithm is proposed in the deployment step of Cross Industry Standard Process for Data Mining (CRISP-DM) model. According to the obtained results, the hybrid algorithm performs a better accuracy in compare to ANN technique itself. (C) 2019 Elsevier Ltd. All rights reserved.
| Item Type: | Article |
|---|---|
| Funders: | Ministry of Higher Education, Malaysia [Grant No: IIRG007A, PG144-2016A], Universiti Malaya |
| Uncontrolled Keywords: | Data mining-based damage identification; Experimental modal analysis; Cross Industry Standard Process for Data; Mining; Imperial competitive algorithm; Hybrid algorithm |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering > Department of Civil Engineering |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 07 Nov 2024 00:45 |
| Last Modified: | 07 Nov 2024 00:45 |
| URI: | http://eprints.um.edu.my/id/eprint/36919 |
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