Prediction of small-scale piles by considering lateral deflection based on Elman Neural Network - Improved Arithmetic Optimizer algorithm

Piles (kinds of geotechnical structures) are used for resisting various lateral loads including earthquakes and inclined loads. Hence, these structures' behavior under lateral load should be studied. Therefore, this investigation studies the lateral deflection (LD) of piles under different situations. 192 physical models were carried out by consideration of the most important factor on the lateral deflection amounts in dried sandy soils. Besides, a model of the Elman Neural Network (ENN) - Improved Arithmetic Optimizer (IAO) algorithm was suggested for predicting the piles' lateral deflection. For the intention of comparison, the Elman Neural Network model and Particle Swarm Optimization - Artificial Neural Network were utilized in lateral deflection amounts estimation. For evaluating the proposed model validity, some parameters like Variance Account For, determination coefficient, and Root Mean Squared Error were estimated. The results showed the ENN-IAO method is more reliable for lateral deflection prediction in a small-scale pile in comparison to the ENN method and PSO-ANN model.