Influence of feature selection on machine learning prediction of pile foundation – The role of soil-pile interaction knowledge and application to base resistance

Nguyen, TT; Le Nguyen, K; Huynh, TQ; Tran, Q

Influence of feature selection on machine learning prediction of pile foundation – The role of soil-pile interaction knowledge and application to base resistance

Nguyen, TT Le Nguyen, K Huynh, TQ Tran, Q

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Geodata and AI, 2025, 3, pp. 100019
Issue Date:: 2025-06

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Field	Value	Language
dc.contributor.author	Nguyen, TT
dc.contributor.author	Le Nguyen, K
dc.contributor.author	Huynh, TQ
dc.contributor.author	Tran, Q
dc.date.accessioned	2026-03-06T05:53:30Z
dc.date.available	2026-03-06T05:53:30Z
dc.date.issued	2025-06
dc.identifier.citation	Geodata and AI, 2025, 3, pp. 100019
dc.identifier.issn	3050-483X
dc.identifier.uri	http://hdl.handle.net/10453/193781
dc.description.abstract	The use of machine learning (ML) to predict and classify various aspects of pile foundation has rapidly gained paramount attention over the past decade, resulting in various sets of input features and algorithms that have been employed. This study classifies those input features into 2 groups, i.e., with and without understanding of soil-pile interaction, and examines their impacts on the development and performance of ML models. Six different ML algorithms covering from the most fundamental decision trees to advanced bagging and boosting techniques are adopted. An extensive database gathering 86 cases (1129 datapoints) of pile load tests in complex soft soil region is developed and employed to investigate the influence that 2 different sets of input features can have on predicting base resistance of piles. Through a comprehensive training-to-validation process, the results prove the importance of having soil-pile interaction features such as the load-displacement curves in optimizing the ML prediction of base resistance. On the other hand, the use of only soil and pile features for model inputs can help reduce the cost for pile tests, though it can lead to poorer prediction performance. Not only does the current study establish novel models based on machine learning to predict base resistance of piles, but it also employs quantitative feature analysis to gain insight into the load-transfer process, where the interaction between soil and pile develops downward from side to base resistances. The study proposes an adaptive training strategy that effectively improves model application to new contexts, reducing the need for extensive data collection and field survey costs, thereby enhancing the cost-effectiveness and scalability of ML deployments in geotechnical engineering.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Geodata and AI
dc.relation.isbasedon	10.1016/j.geoai.2025.100019
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Influence of feature selection on machine learning prediction of pile foundation – The role of soil-pile interaction knowledge and application to base resistance
dc.type	Journal Article
utslib.citation.volume	3
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Transport Research Centre (TRC)
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2026-03-06T05:53:10Z
pubs.publication-status	Published
pubs.volume	3

Abstract:

The use of machine learning (ML) to predict and classify various aspects of pile foundation has rapidly gained paramount attention over the past decade, resulting in various sets of input features and algorithms that have been employed. This study classifies those input features into 2 groups, i.e., with and without understanding of soil-pile interaction, and examines their impacts on the development and performance of ML models. Six different ML algorithms covering from the most fundamental decision trees to advanced bagging and boosting techniques are adopted. An extensive database gathering 86 cases (1129 datapoints) of pile load tests in complex soft soil region is developed and employed to investigate the influence that 2 different sets of input features can have on predicting base resistance of piles. Through a comprehensive training-to-validation process, the results prove the importance of having soil-pile interaction features such as the load-displacement curves in optimizing the ML prediction of base resistance. On the other hand, the use of only soil and pile features for model inputs can help reduce the cost for pile tests, though it can lead to poorer prediction performance. Not only does the current study establish novel models based on machine learning to predict base resistance of piles, but it also employs quantitative feature analysis to gain insight into the load-transfer process, where the interaction between soil and pile develops downward from side to base resistances. The study proposes an adaptive training strategy that effectively improves model application to new contexts, reducing the need for extensive data collection and field survey costs, thereby enhancing the cost-effectiveness and scalability of ML deployments in geotechnical engineering.

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http://hdl.handle.net/10453/193781