Toghroli, Ali and Suhatril, Meldi and Ibrahim, Zainah and Safa, Maryam and Shariati, Mahdi and Shamshirband, Shahaboddin (2018) Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam. Journal of Intelligent Manufacturing, 29 (8). pp. 1793-1801. ISSN 0956-5515, DOI https://doi.org/10.1007/s10845-016-1217-y.
Full text not available from this repository.Abstract
Evaluation of the parameters affecting the shear strength and ductility of steel–concrete composite beam is the goal of this study. This study focuses on predicting the future output of beam’s strength and ductility based on relative inputs using a soft computing scheme, extreme learning machine (ELM). Estimation and prediction results of the ELM models were compared with genetic programming (GP) and artificial neural networks (ANNs) models. Referring to the experimental results, as opposed to the GP and ANN methods, the ELM approach enhanced generalization ability and predictive accuracy. Moreover, achieved results indicated that the developed ELM models can be used with confidence for further work on formulating novel model predictive strategy in shear strength and ductility of steel concrete composite. Furthermore, the experimental results indicate that on the whole, the newflanged algorithm creates good generalization presentation. In comparison to the other widely used conventional learning algorithms, the ELM has a much faster learning ability.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Composite; Extreme learning machine (ELM); Prediction; Steel–concrete composite beam |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Computer Science & Information Technology Faculty of Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 25 Feb 2019 02:30 |
| Last Modified: | 25 Feb 2019 02:30 |
| URI: | http://eprints.um.edu.my/id/eprint/20462 |
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