Prediction of Load Capacities of Closed-Ended Piles Using Boosting Machine Learning Methods

Karakaş S., Ülker M. B. C., Taşkın G.

5th International Conference on New Developments in Soil Mechanics and Geotechnical Engineering, ZM 2022, Virtual, Online, 30 June - 02 July 2022, vol.305, pp.225-233 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 305
  • Doi Number: 10.1007/978-3-031-20172-1_21
  • City: Virtual, Online
  • Page Numbers: pp.225-233
  • Keywords: Boosting algorithms, Closed-ended piles, CPT test, Load-bearing capacity, Machine learning, Shapley method
  • Istanbul Technical University Affiliated: Yes


In this study, a novel data-driven model is developed using boosting-type machine learning algorithms with the aim of predicting the ultimate load-bearing capacities of closed-ended piles. A comprehensive database is gathered using the full-scale load test data with four features. Special boosting type machine learning methods are trained and tested with the database. Once predictions are made, a newly developed machine learning algorithm called Shapley method is utilized to decide the effectiveness of the selected features in predicting pile capacities. Results indicate that the pile cross-section area and length features are sufficient to achieve accurate predictions covering the parameters on the pile side and the CPT-based tip resistance is the only parameter needed on the soil side. While different boosting methods result in different levels of accuracy in predicting the load bearing capacities of closed-ended piles, it is generally possible to determine the minimum number of features necessary to satisfy a high goodness of fit. In the end, optimum number of features are determined in the prediction process using the Shapley method through the boosting algorithms giving us a valuable prediction tool for estimating the bearing capacity of closed-ended piles.