Ganasan, Reventheran and Tan, Chee Ghuan and Ibrahim, Zainah and Nazri, Fadzli Mohamed and Sherif, Muhammad M. and El-Shafie, Ahmed (2021) Development of crack width prediction models for RC beam-column joint subjected to lateral cyclic loading using machine learning. Applied Sciences-Basel, 11 (16). ISSN 2076-3417, DOI https://doi.org/10.3390/app11167700.
Full text not available from this repository.Abstract
In recent years, researchers have investigated the development of artificial neural networks (ANN) and finite element models (FEM) for predicting crack propagation in reinforced concrete (RC) members. However, most of the developed prediction models have been limited to focus on individual isolated RC members without considering the interaction of members in a structure subjected to hazard loads, due to earthquake and wind. This research develops models to predict the evolution of the cracks in the RC beam-column joint (BCJ) region. The RC beam-column joint is subjected to lateral cyclic loading. Four machine learning models are developed using Rapidminer to predict the crack width experienced by seven RC beam-column joints. The design parameters associated with RC beam-column joints and lateral cyclic loadings in terms of drift ratio are used as inputs. Several prediction models are developed, and the highest performing neural networks are selected, refined, and optimized using the various split data ratios, number of inputs, and performance indices. The error in predicting the experimental crack width is used as a performance index.
| Item Type: | Article |
|---|---|
| Funders: | University of Malaya (RU GPF007A-2019) |
| Uncontrolled Keywords: | Crack widths; RC beam-column joint; Drift ratio; Machine learning; Prediction models |
| Subjects: | Q Science > QC Physics Q Science > QD Chemistry |
| Divisions: | Faculty of Engineering > Department of Civil Engineering |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 29 Mar 2022 07:56 |
| Last Modified: | 29 Mar 2022 07:56 |
| URI: | http://eprints.um.edu.my/id/eprint/28294 |
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