Innovative Approach for Moment Capacity Estimation of Spirally Reinforced Concrete Columns Using Swarm Intelligence–Based Algorithms and Neural Network

Abstract

The purpose of this paper is to present an innovative equation to predict the moment capacity of spirally reinforced concrete columns with high accuracy using a combination of neural network and metaheuristic optimization algorithms. To this end, a large experimental database has been gathered to train a neural network with seven independent parameters that deal with the dimensional properties of the columns, reinforcements, materials, and also the forces. Furthermore, the authors improved the process of training with consideration of two optimization techniques: particle swarm optimization (PSO) and Harris hawks optimization (HHO). Then, the best model was selected to a statistical methodology to extract an empirical equation to predict the target, which makes the proposed system of this article more applicable, especially for the practical usages. The results indicated that the neural network with the PSO algorithm had better results than the other model. Also, it has been found that the proposed formulation could predict the moment capacity of the considered element with high performance. The presented equation of this article has many applications in civil engineering, such as retrofitting and rehabilitation.