Factors Influencing Pile Friction Bearing Capacity: Proposing a Novel Procedure Based on Gradient Boosted Tree Technique

Chia Yu Huat, Seyed Mohammad Hossein Moosavi, Ahmed Salih Mohammed, Danial Jahed Armaghani, Dmitrii Vladimirovich Ulrikh, Masoud Monjezi and Sai Hin Lai
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Chia Yu Huat: Department of Civil Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia
Seyed Mohammad Hossein Moosavi: Department of Civil Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia
Ahmed Salih Mohammed: Civil Engineering Department, College of Engineering, University of Sulaimani, Sulaymaniyah 46001, Iraq
Danial Jahed Armaghani: Department of Urban Planning, Engineering Networks and Systems, Institute of Architecture and Construction, South Ural State University, 454080 Chelyabinsk, Russia
Dmitrii Vladimirovich Ulrikh: Department of Urban Planning, Engineering Networks and Systems, Institute of Architecture and Construction, South Ural State University, 454080 Chelyabinsk, Russia
Masoud Monjezi: Department of Mining, Faculty of Engineering, Tarbiat Modares University, Tehran 14115-143, Iran
Sai Hin Lai: Department of Civil Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia

Sustainability, 2021, vol. 13, issue 21, 1-23

Abstract: 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.

Keywords: tree-based techniques; feature selection; pile bearing capacity; gradient boosted tree; random forest (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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