Tree-Based Solution Frameworks for Predicting Tunnel BoringMachine Performance Using RockMass andMaterial Properties

Armaghani, DJ; Liu, Z; Khabbaz, H; Fattahi, H; Li, D; Afrazi, M

Tree-Based Solution Frameworks for Predicting Tunnel BoringMachine Performance Using RockMass andMaterial Properties

Armaghani, DJ Liu, Z Khabbaz, H Fattahi, H Li, D Afrazi, M

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Publisher:: Tech Science Press
Publication Type:: Journal Article
Citation:: CMES - Computer Modeling in Engineering and Sciences, 2024, 141, (3), pp. 2421-2451
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Armaghani, DJ
dc.contributor.author	Liu, Z
dc.contributor.author	Khabbaz, H
dc.contributor.author	Fattahi, H
dc.contributor.author	Li, D
dc.contributor.author	Afrazi, M
dc.date.accessioned	2025-01-06T02:44:36Z
dc.date.available	2025-01-06T02:44:36Z
dc.date.issued	2024-01-01
dc.identifier.citation	CMES - Computer Modeling in Engineering and Sciences, 2024, 141, (3), pp. 2421-2451
dc.identifier.issn	1526-1492
dc.identifier.issn	1526-1506
dc.identifier.uri	http://hdl.handle.net/10453/183007
dc.description.abstract	Tunnel Boring Machines (TBMs) are vital for tunnel and underground construction due to their high safety and efficiency. Accurately predicting TBM operational parameters based on the surrounding environment is crucial for planning schedules and managing costs. This study investigates the effectiveness of tree-based machine learning models, including Random Forest, Extremely Randomized Trees, Adaptive Boosting Machine, Gradient Boosting Machine, Extreme Gradient Boosting Machine (XGBoost), Light Gradient Boosting Machine, and CatBoost, in predicting the Penetration Rate (PR) of TBMs by considering rock mass and material characteristics. These techniques are able to provide a good relationship between input(s) and output parameters; hence, obtaining a high level of accuracy. To do that, a comprehensive database comprising various rock mass and material parameters, including Rock Mass Rating, Brazilian Tensile Strength, andWeathering Zone, was utilized for model development. The practical application of these models was assessed with a new dataset representing diverse rock mass and material properties. To evaluate model performance, ranking systems and Taylor diagrams were employed. CatBoost emerged as the most accurate model during training and testing, with R2 scores of 0.927 and 0.861, respectively. However, during validation, XGBoost demonstrated superior performance with an R2 of 0.713. Despite these variations, all tree-based models showed promising accuracy in predicting TBM performance, providing valuable insights for similar projects in the future.
dc.language	en
dc.publisher	Tech Science Press
dc.relation.ispartof	CMES - Computer Modeling in Engineering and Sciences
dc.relation.isbasedon	10.32604/cmes.2024.052210
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0102 Applied Mathematics, 0103 Numerical and Computational Mathematics, 0915 Interdisciplinary Engineering
dc.subject.classification	Applied Mathematics
dc.subject.classification	4901 Applied mathematics
dc.subject.classification	4903 Numerical and computational mathematics
dc.title	Tree-Based Solution Frameworks for Predicting Tunnel BoringMachine Performance Using RockMass andMaterial Properties
dc.type	Journal Article
utslib.citation.volume	141
utslib.for	0102 Applied Mathematics
utslib.for	0103 Numerical and Computational Mathematics
utslib.for	0915 Interdisciplinary 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
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Technology in Water and Wastewater (CTWW)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Transport Research Centre (TRC)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Advanced Modelling and Geospatial lnformation Systems (CAMGIS)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Built Infrastructure Resilience (CBIR)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Technology in Water and Wastewater (CTWW)/Centre for Technology in Water and Wastewater (CTWW) Associate Members
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	2025-01-06T02:44:34Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	141
utslib.citation.issue	3

Abstract:

Tunnel Boring Machines (TBMs) are vital for tunnel and underground construction due to their high safety and efficiency. Accurately predicting TBM operational parameters based on the surrounding environment is crucial for planning schedules and managing costs. This study investigates the effectiveness of tree-based machine learning models, including Random Forest, Extremely Randomized Trees, Adaptive Boosting Machine, Gradient Boosting Machine, Extreme Gradient Boosting Machine (XGBoost), Light Gradient Boosting Machine, and CatBoost, in predicting the Penetration Rate (PR) of TBMs by considering rock mass and material characteristics. These techniques are able to provide a good relationship between input(s) and output parameters; hence, obtaining a high level of accuracy. To do that, a comprehensive database comprising various rock mass and material parameters, including Rock Mass Rating, Brazilian Tensile Strength, andWeathering Zone, was utilized for model development. The practical application of these models was assessed with a new dataset representing diverse rock mass and material properties. To evaluate model performance, ranking systems and Taylor diagrams were employed. CatBoost emerged as the most accurate model during training and testing, with R2 scores of 0.927 and 0.861, respectively. However, during validation, XGBoost demonstrated superior performance with an R2 of 0.713. Despite these variations, all tree-based models showed promising accuracy in predicting TBM performance, providing valuable insights for similar projects in the future.

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http://hdl.handle.net/10453/183007