Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam
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 (EL...
| Main Authors: | , , , , , |
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| Format: | Article |
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Springer Verlag
2018
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| _version_ | 1796960886116581376 |
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| author | Toghroli, Ali Suhatril, Meldi Ibrahim, Zainah Safa, Maryam Shariati, Mahdi Shamshirband, Shahaboddin |
| author_facet | Toghroli, Ali Suhatril, Meldi Ibrahim, Zainah Safa, Maryam Shariati, Mahdi Shamshirband, Shahaboddin |
| author_sort | Toghroli, Ali |
| collection | UM |
| description | 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. |
| first_indexed | 2024-03-06T05:51:17Z |
| format | Article |
| id | um.eprints-20462 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:51:17Z |
| publishDate | 2018 |
| publisher | Springer Verlag |
| record_format | dspace |
| spelling | um.eprints-204622019-02-25T02:30:14Z http://eprints.um.edu.my/20462/ Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam Toghroli, Ali Suhatril, Meldi Ibrahim, Zainah Safa, Maryam Shariati, Mahdi Shamshirband, Shahaboddin QA75 Electronic computers. Computer science TA Engineering (General). Civil engineering (General) 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. Springer Verlag 2018 Article PeerReviewed 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 <https://doi.org/10.1007/s10845-016-1217-y>. https://doi.org/10.1007/s10845-016-1217-y doi:10.1007/s10845-016-1217-y |
| spellingShingle | QA75 Electronic computers. Computer science TA Engineering (General). Civil engineering (General) Toghroli, Ali Suhatril, Meldi Ibrahim, Zainah Safa, Maryam Shariati, Mahdi Shamshirband, Shahaboddin Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam |
| title | Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam |
| title_full | Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam |
| title_fullStr | Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam |
| title_full_unstemmed | Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam |
| title_short | Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam |
| title_sort | potential of soft computing approach for evaluating the factors affecting the capacity of steel concrete composite beam |
| topic | QA75 Electronic computers. Computer science TA Engineering (General). Civil engineering (General) |
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