Developing Machine Learning Models to Generate the Load-Settlement Curves of Piles from Cone Penetration Test Data

Document Type

Conference Proceeding

Publication Date

1-1-2025

Abstract

This paper explores the potential application of machine learning (ML) algorithms to evaluate the load-settlement curve of piles from cone penetration test (CPT) data. Several ML models, such as artificial neural network (ANN), random forest (RF), and gradient boosted tree (GBT), were developed using a database of 64 static pile load tests (PLTs) and corresponding CPT data. The load-settlement curves predicted using the developed ML models were compared with the measured curves from PLTs and those predicted using conventional load-transfer methods on the same test data. The results show that the ML models have great potential in predicting the load-settlement behavior of axially loaded single piles from CPT data that outperform the conventional load-transfer methods.

Publication Source (Journal or Book title)

Geotechnical Special Publication

First Page

104

Last Page

113

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