Steel and Composite Structures

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Modelling the deflection of reinforced concrete beams using the improved artificial neural network by imperialist competitive optimization

  • Li, Ning (School of Resource and Environment Engineering, Wuhan University of Technology) ;
  • Asteris, Panagiotis G. (Computational Mechanics Laboratory, School of Pedagogical and Technological Education) ;
  • Tran, Trung-Tin (Department of Information Technology, FPT University) ;
  • Pradhan, Biswajeet (Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, Faculty of Engineering & IT, University of Technology Sydney) ;
  • Nguyen, Hoang (Hanoi University of Mining and Geology)
  • Received : 2020.10.23
  • Accepted : 2022.03.17
  • Published : 2022.03.25
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Abstract

This study proposed a robust artificial intelligence (AI) model based on the social behaviour of the imperialist competitive algorithm (ICA) and artificial neural network (ANN) for modelling the deflection of reinforced concrete beams, abbreviated as ICA-ANN model. Accordingly, the ICA was used to adjust and optimize the parameters of an ANN model (i.e., weights and biases) aiming to improve the accuracy of the ANN model in modelling the deflection reinforced concrete beams. A total of 120 experimental datasets of reinforced concrete beams were employed for this aim. Therein, applied load, tensile reinforcement strength and the reinforcement percentage were used to simulate the deflection of reinforced concrete beams. Besides, five other AI models, such as ANN, SVM (support vector machine), GLMNET (lasso and elastic-net regularized generalized linear models), CART (classification and regression tree) and KNN (k-nearest neighbours), were also used for the comprehensive assessment of the proposed model (i.e., ICA-ANN). The comparison of the derived results with the experimental findings demonstrates that among the developed models the ICA-ANN model is that can approximate the reinforced concrete beams deflection in a more reliable and robust manner.

Keywords

Acknowledgement

This research was supported by the Supported by the National Key R&D Program of China (Grant No. 2019YFC0605304).

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