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Article

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

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Department of Civil Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia
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Civil Engineering Department, College of Engineering, University of Sulaimani, Sulaymaniyah 46001, Iraq
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Department of Urban Planning, Engineering Networks and Systems, Institute of Architecture and Construction, South Ural State University, 454080 Chelyabinsk, Russia
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Department of Mining, Faculty of Engineering, Tarbiat Modares University, Tehran 14115-143, Iran
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Authors to whom correspondence should be addressed.
Academic Editor: Anjui Li
Sustainability 2021, 13(21), 11862; https://doi.org/10.3390/su132111862
Received: 21 August 2021 / Revised: 25 October 2021 / Accepted: 25 October 2021 / Published: 27 October 2021
(This article belongs to the Special Issue Advances in Rock Mechanics and Geotechnical Engineering)
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 (R2) 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. View Full-Text
Keywords: tree-based techniques; feature selection; pile bearing capacity; gradient boosted tree; random forest tree-based techniques; feature selection; pile bearing capacity; gradient boosted tree; random forest
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MDPI and ACS Style

Huat, C.Y.; Moosavi, S.M.H.; Mohammed, A.S.; Armaghani, D.J.; Ulrikh, D.V.; Monjezi, M.; Hin Lai, S. Factors Influencing Pile Friction Bearing Capacity: Proposing a Novel Procedure Based on Gradient Boosted Tree Technique. Sustainability 2021, 13, 11862. https://doi.org/10.3390/su132111862

AMA Style

Huat CY, Moosavi SMH, Mohammed AS, Armaghani DJ, Ulrikh DV, Monjezi M, Hin Lai S. Factors Influencing Pile Friction Bearing Capacity: Proposing a Novel Procedure Based on Gradient Boosted Tree Technique. Sustainability. 2021; 13(21):11862. https://doi.org/10.3390/su132111862

Chicago/Turabian Style

Huat, Chia Y., Seyed M.H. Moosavi, Ahmed S. Mohammed, Danial J. Armaghani, Dmitrii V. Ulrikh, Masoud Monjezi, and Sai Hin Lai. 2021. "Factors Influencing Pile Friction Bearing Capacity: Proposing a Novel Procedure Based on Gradient Boosted Tree Technique" Sustainability 13, no. 21: 11862. https://doi.org/10.3390/su132111862

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