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.
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