Exploring Tree-Based Machine Learning Models to Estimate the Ultimate Pile Capacity From Cone Penetration Test Data

Abstract

Several approaches have been developed to estimate the ultimate capacity of piles, such as static and dynamic load tests, static analysis from soil borings, and directly utilizing in-situ test results. Recently, there has been increased interest in using in-situ cone penetration test (CPT) to estimate pile capacity. Several analytical pile-CPT methods have been developed, which involve several correlation assumptions that can affect their accuracy. In this paper, three tree-based machine learning (ML) models, namely decision tree (DT), random forest (RF), and gradient boosted tree (GBT), are developed for estimating the ultimate capacity of piles from CPT data. A database that contains 80 pile load tests and associated CPT data collected in Louisiana was used to develop these ML models. The measured ultimate pile capacity (Qm) was determined using Davisson’s interpretation method from the load–settlement curve of each pile load test. Among the developed ML models, GBT demonstrated the most accurate ML model compared with the others. The estimation of ultimate pile capacity from the GBT model is compared with those obtained from the four best-performing direct pile-CPT methods (based on a previous study): the University of Florida (UF), probabilistic, European Regional Technical Committee 3 (ERTC3), and Laboratoire Central des Ponts et Chaussées (LCPC) methods. The GBT and pile-CPT methods were evaluated and ranked based on analysis of multiple statistical criteria. The results clearly showed that the GBT model outperforms the four direct pile-CPT methods for estimating the ultimate capacity of piles.