Prediction of Lateral Deflection of Small-Scale Piles Using Hybrid PSO–ANN Model

Piles, as a type of geotechnical structures, are widely utilized to resist different lateral loads sources such as inclined loads and earthquakes. Therefore, the behavior of such structures under lateral loads needs to be investigated. Accordingly, this study examines the piles’ lateral deflection (LD) under various conditions. A total of 183 physical modeling tests were conducted in laboratory considering the most influential parameters on LD values in dried sandy soils. Additionally, a new hybrid model of particle swarm optimization (PSO)–artificial neural network (ANN) was proposed to predict LD of the piles. For comparison purposes, a pre-developed ANN model was also designed for estimation of LD values. In order to evaluate the prediction accuracy of the developed models, several performance indices such as root-mean-squared error (RMSE), coefficient of determination (R²), and variance account for were calculated. The proposed PSO–ANN model was found capable of providing a high accuracy level and, at the same time, a low system error in the LD prediction process. The RMSE values of 0.072 and 0.085 were determined, respectively, for training and testing datasets of the developed PSO–ANN model, while these values were 0.121 and 0.103 for the same datasets of the ANN predictive technique, respectively. It can be concluded that the PSO–ANN model can be relied on as a new hybrid model in field of this study, and also it can be used in other related studies with caution.