Factors influencing pile friction bearing capacity: Proposing a novel procedure based on gradient boosted tree technique

Huat, Chia Yu and Moosavi, Seyed Mohammad Hossein and Mohammed, Ahmed Salih and Armaghani, Danial Jahed and Ulrikh, Dmitrii Vladimirovich and Monjezi, Masoud and Lai, Sai Hin (2021) Factors influencing pile friction bearing capacity: Proposing a novel procedure based on gradient boosted tree technique. Sustainability, 13 (21). ISSN 2071-1050, DOI https://doi.org/10.3390/su132111862.

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In geotechnical engineering, there is a need to propose a practical, reliable and accurate way for the estimation of pile bearing capacity. A direct measure of this parameter is difficult and expensive to achieve on-site, and needs a series of machine settings. This study aims to introduce a process for selecting the most important parameters in the area of pile capacity and to propose several tree-based techniques for forecasting the pile bearing capacity, all of which are fully intelligent. In terms of the first objective, pile length, hammer drop height, pile diameter, hammer weight, and N values of the standard penetration test were selected as the most important factors for estimating pile capacity. These were then used as model inputs in different tree-based techniques, i.e., decision tree (DT), random forest (RF), and gradient boosted tree (GBT) in order to predict pile friction bearing capacity. This was implemented with the help of 130 High Strain Dynamic Load tests which were conducted in the Kepong area, Malaysia. The developed tree-based models were assessed using various statistical indices and the best performance with the lowest system error was obtained by the GBT technique. The coefficient of determination (R-2) values of 0.901 and 0.816 for the train and test parts of the GBT model, respectively, showed the power and capability of this tree-based model in estimating pile friction bearing capacity. The GBT model and the input selection process proposed in this research can be introduced as a new, powerful, and practical methodology to predict pile capacity in real projects.

Item Type: Article
Funders: Act 211 Government of the Russian Federation [02.A03.21.0011]
Uncontrolled Keywords: Tree-based techniques; Feature selection; Pile bearing capacity; Gradient boosted tree; Random forest
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Engineering > Department of Civil Engineering
Depositing User: Ms Zaharah Ramly
Date Deposited: 12 Apr 2022 04:24
Last Modified: 12 Apr 2022 04:49
URI: http://eprints.um.edu.my/id/eprint/28678

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