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

S. Karakaş, M. B.C. Ülker*, G. Taşkın

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication5th International Conference on New Developments in Soil Mechanics and Geotechnical Engineering - Proceedings of ZM 2022
EditorsCavit Atalar, Feyza Çinicioğlu
PublisherSpringer Science and Business Media Deutschland GmbH
Pages225-233
Number of pages9
ISBN (Print)9783031201714
DOIs
Publication statusPublished - 2023
Event5th International Conference on New Developments in Soil Mechanics and Geotechnical Engineering, ZM 2022 - Virtual, Online
Duration: 30 Jun 20222 Jul 2022

Publication series

NameLecture Notes in Civil Engineering
Volume305
ISSN (Print)2366-2557
ISSN (Electronic)2366-2565

Conference

Conference5th International Conference on New Developments in Soil Mechanics and Geotechnical Engineering, ZM 2022
CityVirtual, Online
Period30/06/222/07/22

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

  • Boosting algorithms
  • Closed-ended piles
  • CPT test
  • Load-bearing capacity
  • Machine learning
  • Shapley method

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