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Re-evaluation of machine learning models for predicting ultimate bearing capacity of piles through SHAP and Joint Shapley methods

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Abstract

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. Using updated input features, the models are rebuilt and predictions are repeated with the new input feature set; hence, the re-evaluation of ML models is focused on at this point. These features are twofold: One has two geometric features attributed to piles: cross-section area and length, and the other is a single feature attributed to soil, the average CPT tip resistance, which are overall sufficient in predicting the load capacity of both closed and open-ended piles. To the best of our knowledge, this study is one of the pioneers of its kind for pile foundations. The results show that the predictions of ML methods aided with strong interpretability techniques prove to be necessary in providing accurate results. As a result, Shapley is determined to be a useful tool for other geotechnical engineering applications as well.

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Data availability

The datasets generated during and/or analyzed during the current study are not publicly available due to the reason that proper permissions must be obtained from TUBITAK owning the data, which is funding this study. Upon such permission, the data will be available from the corresponding author upon reasonable request.

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Acknowledgements

Authors would like to acknowledge the financial support of Scientific and Technological Council of Turkey (TÜBİTAK) through the research project with number 121M736.

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Authors and Affiliations

  1. Payment Systems, Garanti BBVA, Istanbul, Turkey

    S. Karakaş

  2. Earthquake Engineering Program, Istanbul Technical University Institute of Disaster Management, 34469, Istanbul, Turkey

    G. Taşkın & M. B. C. Ülker

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Karakaş, S., Taşkın, G. & Ülker, M.B.C. Re-evaluation of machine learning models for predicting ultimate bearing capacity of piles through SHAP and Joint Shapley methods. Neural Comput & Applic 36, 697–715 (2024). https://doi.org/10.1007/s00521-023-09053-3

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