Machine Learning–Aided Prediction of Shaft Resistance and Bearing Capacity Factors for Pile Foundation Design Using CPT Data

Abstract

This study presents a data-driven framework for predicting the shaft resistance ( β ) and end bearing capacity ( N t ) factors of piles using cone penetration test (CPT) data. Traditional design methods often rely on empirical values that oversimplify soil–pile interaction by neglecting the influence of pile geometry. To address this, this research employs an evolutionary polynomial regression with a multiobjective genetic algorithm (EPR-MOGA) to develop design equations. A two-step methodology was used: first, robust models for shaft capacity ( Q s ) and end-bearing capacity ( Q t ) of drilled piles were developed from databases of 54 and 31 load tests, respectively. Validated using fivefold cross-validation, these base models demonstrated high predictive accuracy, achieving an average coefficient of determination of 0.92 for Q s and 0.97 for Q t . Explicit equations for the β and N t factors were then derived from these validated models. These equations inherently account for complex soil–pile interactions, showing increases with soil friction angle and reductions with pile slenderness ratio ( L / D ) at a diminishing rate. This eliminates the need for the additional caps on effective vertical stress required by conventional methods. Finally, the models were extended to driven piles by calibrating modification factors on an independent 36-test database, yielding values of 1.46 for Q s and 2.38 for Q t .