Developing Tree-Based Machine Learning Models for Estimating the Pile Setup Parameter for Clay Soils

Abstract

Piles driven into cohesive soils usually experience increases in capacity with time, known as pile setup phenomenon. Several empirical methods have been developed to estimate the setup parameter (A), such as the well-known Skov and Denver equation. Parameter A is crucial in predicting pile setup behavior. In this study, tree-based machine learning (ML) models such as random forest (RF) and gradient boosted tree (GBT) were applied for better estimation of the setup parameter. A database consisting of setup data from 12 instrumented piles tested at different times, and corresponding cone penetration test (CPT) and soil boring data were collected. The soil properties (i.e., undrained shear strength, plasticity index, over consolidation ratio, and coefficient of consolidation) and CPT data (cone tip resistance, sleeve friction) of clayey soil layers at pile locations were utilized to develop the ML models. Three types of tree-based ML model were developed for predicting the setup parameter, A, using CPT and soil boring data. A comparison was made between the developed ML models based on soil properties, ML models based on CPT data, and an artificial neural network (ANN) model proposed in a previous study using the same dataset. Furthermore, the best performing ML models were compared with two nonlinear regression models recommended in a previous study using the same dataset for estimating the setup parameter. The results of this research clearly demonstrated the superior prediction capability of the tree-based ML models, particularly the GBT model over the ANN and the two nonlinear regression models in evaluating the pile setup parameter.