Özet
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.
Orijinal dil | İngilizce |
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Ana bilgisayar yayını başlığı | 5th International Conference on New Developments in Soil Mechanics and Geotechnical Engineering - Proceedings of ZM 2022 |
Editörler | Cavit Atalar, Feyza Çinicioğlu |
Yayınlayan | Springer Science and Business Media Deutschland GmbH |
Sayfalar | 225-233 |
Sayfa sayısı | 9 |
ISBN (Basılı) | 9783031201714 |
DOI'lar | |
Yayın durumu | Yayınlandı - 2023 |
Etkinlik | 5th International Conference on New Developments in Soil Mechanics and Geotechnical Engineering, ZM 2022 - Virtual, Online Süre: 30 Haz 2022 → 2 Tem 2022 |
Yayın serisi
Adı | Lecture Notes in Civil Engineering |
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Hacim | 305 |
ISSN (Basılı) | 2366-2557 |
ISSN (Elektronik) | 2366-2565 |
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???event.eventtypes.event.conference??? | 5th International Conference on New Developments in Soil Mechanics and Geotechnical Engineering, ZM 2022 |
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Şehir | Virtual, Online |
Periyot | 30/06/22 → 2/07/22 |
Bibliyografik not
Publisher Copyright:© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.