Developing a hybrid PSO–ANN model for estimating the ultimate bearing capacity of rock-socketed piles

Rock-socketed piles are commonly used in foundations built in soft ground, and thus, their bearing capacity is a key issue of universal concern in research, design and construction. The accurate prediction of the ultimate bearing capacity (Q_u) of rock-socketed piles is a difficult task due to the uncertainty surrounding the various factors that affect this capacity. This study was aimed at developing an artificial neural network (ANN) model, as well as a hybrid model based on both particle swarm optimisation (PSO) and ANN, with which to predict the Q_u of rock-socketed piles. PSO, a powerful population-based algorithm used in solving continuous and discrete optimisation problems, was here employed as a robust global search algorithm to determine ANN weights and biases and thereby improve model performance. To achieve the study aims, 132 piles socketed in various rock types as part of the Klang Valley Mass Rapid Transit project, Malaysia, were investigated. Based on previous related investigations, parameters with the most influence on Q_u were identified and utilised in the modelling procedure of the intelligent systems. After constructing and modelling these systems, selected performance indices including the coefficient of determination (R²), root-mean-square error, variance account for and total ranking were used to identify the best models and compare the obtained results. This analysis revealed that the hybrid PSO–ANN model offers a higher degree of accuracy compared to conventional ANN for predicting the Q_u of rock-socketed piles. However, the developed model would be most useful in the preliminary stages of pile design and should be used with caution.