Application of artificial neural network for predicting shaft and tip resistances of concrete piles

Abstract

Axial bearing capacity (ABC) of piles is usually determined by static load test (SLT). However, conducting SLT is costlyand time-consuming. High strain dynamic pile testing (HSDPT) which is provided by pile driving analyzer (PDA)is a more recent approach for predicting the ABC of piles. In comparison to SLT, PDA test is quick and economical.Implementing feed forward back-propagation artificial neural network (ANN) for solving geotechnical problems hasrecently gained attention mainly due to its ability in finding complex nonlinear relationships among different parameters.In this study, an ANN-based predictive model for estimating ABC of piles and its distribution is proposed. For networkconstruction purpose, 36 PDA tests were performed on various concrete piles in different project sites. The PDA results,pile geometrical characteristics as well as soil investigation data were used for training the ANN models. Findingsindicate the feasibility of ANN in predicting ultimate, shaft and tip bearing resistances of piles. The coefficients ofdetermination, R², equal to 0.941, 0.936, and 0.951 for testing data reveal that the shaft, tip and ultimate bearing capacitiesof piles predicted by ANN-based model are in close agreement with those of HSDPT. By using sensitivity analysis, itwas found that the length and area of the piles are dominant factors in the proposed predictive model.