Optimisation of Ensemble Learning Algorithms for Geotechnical Applications: A Mathematical Approach to Relative Density Prediction

Khari, M; Dehghanbanadaki, A; Armaghani, DJ; Khandelwal, M

Optimisation of Ensemble Learning Algorithms for Geotechnical Applications: A Mathematical Approach to Relative Density Prediction

Khari, M Dehghanbanadaki, A Armaghani, DJ Khandelwal, M

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Publisher:: WILEY
Publication Type:: Journal Article
Citation:: Advances in Civil Engineering, 2025, 2025, (1)
Issue Date:: 2025-01-01

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Field	Value	Language
dc.contributor.author	Khari, M
dc.contributor.author	Dehghanbanadaki, A
dc.contributor.author	Armaghani, DJ
dc.contributor.author	Khandelwal, M
dc.contributor.editor	Sun, X
dc.date.accessioned	2026-01-12T04:44:35Z
dc.date.available	2026-01-12T04:44:35Z
dc.date.issued	2025-01-01
dc.identifier.citation	Advances in Civil Engineering, 2025, 2025, (1)
dc.identifier.issn	1687-8086
dc.identifier.issn	1687-8094
dc.identifier.uri	http://hdl.handle.net/10453/191658
dc.description.abstract	The challenge of predicting relative dry density (D<inf>r</inf>) in granular materials is addressed through advanced mathematical modelling and machine learning (ML) techniques. A novel approach to optimise ensemble learning algorithms is presented, with a focus placed on the mathematical foundations of these methods. An experimental dataset obtained from a mobile pluviator was utilised to develop and analyse various ML models. The mathematical analysis was centred on the optimisation and comparative performance of ensemble methods, with particular emphasis given to gradient boosting regression (GBR), AdaBoost regression, and extreme gradient boosting (XGBoost). The mathematical formulation of the GBR model was rigorously examined and optimised using advanced tuning functions, achieving exceptional performance metrics (mean squared error [MSE] = 11.91, mean absolute error [MAE] = 1.93, R<sup>2</sup> = 0.997). Through sensitivity analysis, it was revealed that the distance between the shutter plate and the top sieve is the most significant factor affecting D<inf>r</inf> prediction. A computational platform was developed within the Google Colab environment, demonstrating the practical application of the mathematical models. This research contributes to applied mathematics by showcasing advanced algorithmic approaches to solving complex geotechnical engineering problems while providing a rigorous mathematical foundation for future developments.
dc.language	English
dc.publisher	WILEY
dc.relation.ispartof	Advances in Civil Engineering
dc.relation.isbasedon	10.1155/adce/3042895
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0905 Civil Engineering
dc.subject.classification	4005 Civil engineering
dc.title	Optimisation of Ensemble Learning Algorithms for Geotechnical Applications: A Mathematical Approach to Relative Density Prediction
dc.type	Journal Article
utslib.citation.volume	2025
utslib.for	0905 Civil Engineering
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
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	2026-01-12T04:44:34Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	2025
utslib.citation.issue	1

Abstract:

The challenge of predicting relative dry density (Dr) in granular materials is addressed through advanced mathematical modelling and machine learning (ML) techniques. A novel approach to optimise ensemble learning algorithms is presented, with a focus placed on the mathematical foundations of these methods. An experimental dataset obtained from a mobile pluviator was utilised to develop and analyse various ML models. The mathematical analysis was centred on the optimisation and comparative performance of ensemble methods, with particular emphasis given to gradient boosting regression (GBR), AdaBoost regression, and extreme gradient boosting (XGBoost). The mathematical formulation of the GBR model was rigorously examined and optimised using advanced tuning functions, achieving exceptional performance metrics (mean squared error [MSE] = 11.91, mean absolute error [MAE] = 1.93, R² = 0.997). Through sensitivity analysis, it was revealed that the distance between the shutter plate and the top sieve is the most significant factor affecting Dr prediction. A computational platform was developed within the Google Colab environment, demonstrating the practical application of the mathematical models. This research contributes to applied mathematics by showcasing advanced algorithmic approaches to solving complex geotechnical engineering problems while providing a rigorous mathematical foundation for future developments.

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http://hdl.handle.net/10453/191658