Prediction of axial load bearing capacity of PHC nodular pile using Bayesian regularization artificial neural network

Abstract

Pre-stressed precast high strength concrete (PHC) nodular piles with hyper-MEGA construction method are favorably used in medium to high-rise building foundations. In this study, a feed-forward neural network (FFNN) was adopted to investigate the ultimate axial load bearing capacity of the PHC nodular pile. The network receives the composite pile and geotechnical conditions with eight input neurons and outputs the nodular pile's ultimate axial load bearing capacity. Among numerous possible FFNN network architectures, the most accurate one is determined by optimizing the hidden layer. Network training is conducted with Bayesian regularization backpropagation (BRB); the training datasets consist of static pile load test and standard penetration test index of soil profile collected from various projects in Vietnam. The significance of each input parameter is quantified with importance-based sensitivity analysis. An explicit function has been constructed from weights and bias values at each neuron in the FFNN to estimate the axial load bearing capacity. The excellent agreement of all output values by the proposed FFNN with the measured values proved the model’s robustness and reliability. The predictive capacity of the proposed FFNN model has significantly outperformed all current empirical formulas. The outcome of this study can be directly put into engineering practice to furnish an economically optimal design of the composite nodular pile.

Keywords

• Feed-forward neural network
• Bayesian regularization
• Explicit function
• PHC nodular piles
• hyper-MEGA method
• Ultimate axial load bearing capacity