Steel and Composite Structures

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Hybrid GA-ANN and PSO-ANN methods for accurate prediction of uniaxial compression capacity of CFDST columns

  • Quang-Viet Vu (Laboratory for Computational Civil Engineering, Institute for Computational Science and Artificial Intelligence, Van Lang University) ;
  • Sawekchai Tangaramvong (Center of Excellence in Applied Mechanics and Structures, Department of Civil Engineering, Chulalongkorn University) ;
  • Thu Huynh Van (Center of Excellence in Applied Mechanics and Structures, Department of Civil Engineering, Chulalongkorn University) ;
  • George Papazafeiropoulos (Department of Structural Engineering, National Technical University of Athens)
  • Received : 2023.01.20
  • Accepted : 2023.04.27
  • Published : 2023.06.25
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Abstract

The paper proposes two hybrid metaheuristic optimization and artificial neural network (ANN) methods for the close prediction of the ultimate axial compressive capacity of concentrically loaded concrete filled double skin steel tube (CFDST) columns. Two metaheuristic optimization, namely genetic algorithm (GA) and particle swarm optimization (PSO), approaches enable the dynamic training architecture underlying an ANN model by optimizing the number and sizes of hidden layers as well as the weights and biases of the neurons, simultaneously. The former is termed as GA-ANN, and the latter as PSO-ANN. These techniques utilize the gradient-based optimization with Bayesian regularization that enhances the optimization process. The proposed GA-ANN and PSO-ANN methods construct the predictive ANNs from 125 available experimental datasets and present the superior performance over standard ANNs. Both the hybrid GA-ANN and PSO-ANN methods are encoded within a user-friendly graphical interface that can reliably map out the accurate ultimate axial compressive capacity of CFDST columns with various geometry and material parameters.

Keywords

Acknowledgement

This research was supported by Thailand Science research and Innovation Fund Chulalongkorn University (IND66210025). The support from Ratchadapisek Somphot Fund for Postdoctoral Fellowship and Second Century Fund under Chulalongkorn University are also acknowledged.

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