This study examines the prediction of the load-bearing capacity of closed and open-ended piles using machine-learning (ML) methods. Full-scale load test results and CPT data are used to gather two comprehensive databases for such piles. ML models are developed employing input features associated with pile geometry and CPT resistances along with the ultimate bearing capacity being the only output feature. Following the training/testing sequences, the interpretability of ML predictions is examined through the Shapley and Joint Shapley Value methods. Shapley values for multiple feature combinations allow ML models to decide the number of features necessary to make the most accurate predictions. To the best of our knowledge, this study is one of the pioneers of its kind for pile foundations.

To access this article
DOI: 10.1007/s00521-023-09053-3