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

S. Karakaş, M. B.C. Ülker*, G. Taşkın

*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: ???type-name???Konferans katkısıbilirkişi

1 Atıf (Scopus)

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 dilİngilizce
Ana bilgisayar yayını başlığı5th International Conference on New Developments in Soil Mechanics and Geotechnical Engineering - Proceedings of ZM 2022
EditörlerCavit Atalar, Feyza Çinicioğlu
YayınlayanSpringer Science and Business Media Deutschland GmbH
Sayfalar225-233
Sayfa sayısı9
ISBN (Basılı)9783031201714
DOI'lar
Yayın durumuYayınlandı - 2023
Etkinlik5th International Conference on New Developments in Soil Mechanics and Geotechnical Engineering, ZM 2022 - Virtual, Online
Süre: 30 Haz 20222 Tem 2022

Yayın serisi

AdıLecture Notes in Civil Engineering
Hacim305
ISSN (Basılı)2366-2557
ISSN (Elektronik)2366-2565

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???5th International Conference on New Developments in Soil Mechanics and Geotechnical Engineering, ZM 2022
ŞehirVirtual, Online
Periyot30/06/222/07/22

Bibliyografik not

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

Parmak izi

Prediction of Load Capacities of Closed-Ended Piles Using Boosting Machine Learning Methods' araştırma başlıklarına git. Birlikte benzersiz bir parmak izi oluştururlar.

Alıntı Yap