RETRACTED ARTICLE: Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam

Ali Toghroli, Meldi Suhatril, Zainah Ibrahim, Maryam Safa (), Mahdi Shariati and Shahaboddin Shamshirband
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Ali Toghroli: University of Malaya
Meldi Suhatril: University of Malaya
Zainah Ibrahim: University of Malaya
Maryam Safa: University of Malaya
Mahdi Shariati: University of Malaya
Shahaboddin Shamshirband: University of Malaya

Journal of Intelligent Manufacturing, 2018, vol. 29, issue 8, No 7, 1793-1801

Abstract: 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.

Keywords: Steel–concrete composite beam; Composite; Prediction; Extreme learning machine (ELM) (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10845-016-1217-y

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