Neural Networks for Enhanced Pile Design Coefficients in Effective Stress Beta Method

The versatile, Effective Stress Beta Method can be applied to driven pile design across various soil profiles. However, it is not widely practiced, potentially due to unreliable FHWA guidance for selecting pile design coefficients (β & Nt). This study addresses this issue by evaluating FHWA design coefficients against measured data from the Deep Foundation Load Test Database (DFLTD) V.2. The investigation reveals significant disparities between measured load-carrying capacities (Qm) and capacities calculated using FHWA design coefficients (Qc). The design coefficients, β & Nt, were then systematically back-calculated from load test data and were found to differ from the FHWA design coefficients, as hypothesized. In order to improve the design coefficients selection guidance, a neural network (NN) and machine learning (ML) based approach is proposed. The models BetaSPTNet (Artificial-NN for β) and NtSPTNet (Dense-NN for Nt) outperformed other models in predicting β & Nt. The study showcases NN’s adaptability in handling ambiguous correlations such as the one between geotechnical engineering properties of soil and β & Nt design coefficients. The proposed NN architecture improves precision and reduces uncertainty in determining β and Nt using geotechnical properties derived from SPT and CPT soil exploration data. Finally, we propose a modified approach integrating the traditional Beta design method with NN-predicted design coefficients. This integration significantly enhances the accuracy of calculated pile load-carrying capacities.