Using a genetic algorithm to develop a pile design method

Abstract

A genetic algorithm (GA) was used in this study to develop a standard penetration test (SPT)-based design method for the axial capacity of driven piles. A total of 72 pile load tests was collected from literature and divided into two groups based on their measurements. The first group had the load-transfer distribution measurements for extracting both the unit side and tip resistances. These unit resistances were correlated by the GA with soil measurements and pile properties to develop the design method. The second group, where only the total capacity measurements were available, were used to validate the new design method and compare its performance with three existing SPT-based design methods. The new GA-derived design method considers nonlinear relationships with the effective stress and pile length and provides an unbiased prediction with a low coefficient of variation (COV) of 40.0 %, while the three existing methods overestimate the capacity by a factor of 1.62 to 1.65 with a high COV of 40.3 % to 52.8 %, which could result in an under design of pile foundations. This study shows that the GA was able to obtain complex relationships with great accuracy and the new design method can be applied to new cases reasonably well.