Predicting the Compression Capacity of Screw Piles in Sand Using Machine Learning Trained on Finite Element Analysis

Abstract

Screw piles (often referred to as helical piles) are widely used to resist axial and lateral loads as deep foundations. Multi-helix piles experience complex interactions between the plates which depend on the soil properties, pile stiffness, helix diameter, and the number of helix plates among other factors. Design methods for these piles are typically highly empirical and there remains significant uncertainty around calculating the compression capacity. In this study, a database of 1667 3D finite element analyses was developed to better understand the effect of different inputs on the compression capacity of screw piles in clean sands. Following development of the numerical database, various machine learning methods such as linear regression, neural networks, support vector machines, and Gaussian process regression (GPR) models were trained and tested on the database in order to develop a prediction tool for the pile compression capacity. GPR models, trained on the numerical data, provided excellent predictions of the screw pile compression capacity. The test dataset root mean square error (RMSE) of 29 kN from the GPR model was almost an order of magnitude better than the RMSE of 225 kN from a traditional theoretical approach, highlighting the potential of machine learning methods for predicting the compression capacity of screw piles in homogenous sands.

Keywords

• screw piles
• sand
• finite element analysis
• Plaxis 3D
• machine learning
• Gaussian process regression