Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN

Abstract

The application of artificial neural network (ANN) in predicting pile bearing capacity is underlined in several studies. However, ANN deficiencies in finding global minima as well as its slow rate of convergence are the major drawbacks of implementing this technique. The current study aimed at developing an ANN-based predictive model enhanced with genetic algorithm (GA) optimization technique to predict the bearing capacity of piles. To provide necessary dataset required for establishing the model, 50 dynamic load tests were conducted on precast concrete piles in Pekanbaru, Indonesia. The pile geometrical properties, pile set, hammer weight and drop height were set to be the network inputs and the pile ultimate bearing capacity was set to be the output of the GA-based ANN model. The best predictive model was selected after conducting a sensitivity analysis for determining the optimum GA parameters coupled with a trial-and-error method for finding the optimum network architecture i.e. number of hidden nodes. Results indicate that the pile bearing capacities predicted by GA-based ANN are in close agreement with measured bearing capacities. Coefficient of determination as well as mean square error equal to 0.990 and 0.002 for testing datasets respectively, suggest that implementation of GA-based ANN models as a highly-reliable, efficient and practical tool in predicting the pile bearing capacity is of advantage.