RETRACTED ARTICLE: Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam
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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.
KeywordsSteel–concrete composite beam Composite Prediction Extreme learning machine (ELM)
The study presented herein was made possible by the University of Malaya Research Grant, UMRG RP004D-13AET and the University of Malaya Research Grant, UMRG RP004A-13AET. The authors would like to acknowledge the supports.
- Annema, A. J., Hoen, K., & Wallinga, H. (1994). Precision requirements for single-layer feedforward neural networks, In Fourth international conference on microelectronics for neural networks and fuzzy systems (pp. 145–151).Google Scholar
- Hakim, S. J. S., Noorzaei, J., Jaafar, M., Jameel, M., & Mohammadhassani, M. (2011). Application of artificial neural networks to predict compressive strength of high strength concrete. International Journal of Physical Sciences, 6, 975–981.Google Scholar
- Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2003). Real-time learning capability of neural networks, Technical Report ICIS/45/2003. Singapore: School of Electrical and Electronic Engineering, Nanyang Technological University.Google Scholar
- Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2004). Extreme learning machine: A new learning scheme of feedforward neural networks. International Joint Conference on Neural Networks, 2, 985–990.Google Scholar
- Shariati, M., Ramli Sulong, N. H., & Arabnejad Khanouki, M. M. (2010). Experimental and analytical study on channel shear connectors in light weight aggregate concrete. in Proceedings of the 4th international conference on steel & composite structures, 21–23 July, 2010, Sydney, Australia, 2010. Research Publishing Services, doi: 10.3850/978-981-08-6218-3.
- Shariati, A., Ramli Slong, N. H., Suhatril, M., & Shariati, M. (2012a). Investigation of channel shear connectors for composite concrete and steel T-beam. International Journal of Physical Sciences, 7, 1828–1831.Google Scholar
- Shariati, M., Ramli Sulong, N., Suhatril, M., Shariati, A., Arabnejad Khanouki, M., & Sinaei, H. (2012b). Fatigue energy dissipation and failure analysis of channel shear connector embedded in the lightweight aggregate concrete in composite bridge girders. Fifth international conference on engineering failure analysis 1–4 (July 2012). Hilton Hotel. The Hague: The Netherlands.Google Scholar
- Shariati, M., Ramli Sulong, N. H., Arabnejad Khanouki, M. M., & Mahoutian, M. (2011a). Shear resistance of channel shear connectors in plain, reinforced and lightweight concrete. Scientific Research and Essays, 6, 977–983.Google Scholar
- Shariati, M., Ramli Sulong, N. H., Arabnejad Khanouki, M. M., & Shariati, A. (2011b). Experimental and numerical investigations of channel shear connectors in high strength concrete. In Proceedings of the 2011 world congress on advances in structural engineering and mechanics (ASEM’11+), Seoul, South Korea.Google Scholar
- Shariati, M., Ramli Sulong, N. H., Sinaei, H., Arabnejad Khanouki, M. M., & Shafigh, P. (2011c). Behavior of channel shear connectors in normal and light weight aggregate concrete (experimental and analytical study). Advanced Materials Research, 168, 2303–2307.Google Scholar
- Shariati, M., Ramli Sulong, N. H., Suhatril, M., Shariati, A., Arabnejad Khanouki, M. M., & Sinaei, H. (2013). Comparison of behaviour between channel and angle shear connectors under monotonic and fully reversed cyclic loading. Construction and Building Materials, 38, 582–593.CrossRefGoogle Scholar
- Singh, R., & Balasundaram, S. (2007). Application of extreme learning machine method for time series analysis. International Journal of Intelligent Technology, 2, 256–262.Google Scholar