Base resistance of super-large and long piles in soft soil: performance of artificial neural network model and field implications

Abstract

This study aims to examine the performance of artificial neural network (ANN) model based on 1137 datasets of super-large (1.0–2.5 m in equivalent diameter) and long (40.2–99 m) piles collected over 37 real projects in the past 10 years in Mekong Delta. Five key input parameters including the load, the displacement, the Standard Penetration Test value of the base soil, the distance between the loading point and pile toe, and the axial stiffness are identified via assessing the results of field load tests. Key innovations of this study are (i) use of large database to evaluate the effect that random selection of training and testing datasets can have on the predicted outcomes of ANN modelling, (ii) a simple approach using multiple learning rates to enhance training process, (iii) clarification of the role that the selected input factors can play in the base resistance, and (iv) new empirical relationships between the pile load and settlement. The results show that the random selection of training and testing datasets can affect significantly the predicted results, for example, the confidence of prediction can drop under 80% when an average R2 > 0.85 is required. The analysis indicates predominant role of the displacement in governing the base resistance of piles, providing significant implication to practical designs.