Assessment of tunnel blasting-induced overbreak: A novel metaheuristic-based random forest approach

He, B; Armaghani, DJ; Lai, SH

Assessment of tunnel blasting-induced overbreak: A novel metaheuristic-based random forest approach

He, B Armaghani, DJ Lai, SH

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Tunnelling and Underground Space Technology, 2023, 133, pp. 104979
Issue Date:: 2023-03-01

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Field	Value	Language
dc.contributor.author	He, B
dc.contributor.author	Armaghani, DJ
dc.contributor.author	Lai, SH
dc.date.accessioned	2024-04-04T07:31:02Z
dc.date.available	2024-04-04T07:31:02Z
dc.date.issued	2023-03-01
dc.identifier.citation	Tunnelling and Underground Space Technology, 2023, 133, pp. 104979
dc.identifier.issn	0886-7798
dc.identifier.uri	http://hdl.handle.net/10453/177474
dc.description.abstract	Overbreak is a detrimental phenomenon caused by tunnel blasting, which can lead to increased time and cost in the construction schedule. It is very important to establish a model that can accurately predict the overbreak caused by tunnel blasting. To achieve this goal, the random forest (RF) is an ensemble machine learning model optimised by three metaheuristic algorithms to predicted overbreak, i.e. the grey wolf optimiser (GWO), the whale optimisation algorithm (WOA), and the tunicate swarm algorithm (TSA). The primary roles of GWO, WOA, and TSA are to search for the optimal hyper-parameters of the RF model in the solution space. To create the models above, 523 data samples were taken from a highway tunnel in China. The established database comprised seven predictors or inputs, including the number of holes, hole depth, total charge, advance length, rock mass rating, tunnel cross-sectional area, and powder factor. Three hybrid RF-based models (RF-GWO, RF-WOA, and RF-TSA) were constructed to predict overbreak. Subsequently, the performance levels of the developed hybrid models were evaluated according to four indices: the coefficient of determination, the root mean square error, the variance accounted for, and the A-20 index. The results showed that the TSA optimisation algorithm was better than the other two algorithms (WOA and GWO) at finding the best hyper-parameters for the RF model. Moreover, the results of comparative analysis with the single RF model confirmed that the proposed RF-TSA model is a strong solution with high accuracy for tackling the overbreak issues. The results of this study showed that the developed models can provide more accurate overbreak values compared to the intelligent techniques available in the literature; they can be used in practice and similar projects.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Tunnelling and Underground Space Technology
dc.relation.isbasedon	10.1016/j.tust.2022.104979
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0905 Civil Engineering, 0914 Resources Engineering and Extractive Metallurgy
dc.subject.classification	Geological & Geomatics Engineering
dc.subject.classification	4005 Civil engineering
dc.subject.classification	4019 Resources engineering and extractive metallurgy
dc.title	Assessment of tunnel blasting-induced overbreak: A novel metaheuristic-based random forest approach
dc.type	Journal Article
utslib.citation.volume	133
utslib.for	0905 Civil Engineering
utslib.for	0914 Resources Engineering and Extractive Metallurgy
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	embargoed	*
utslib.copyright.embargo	2025-03-01T00:00:00+1000Z
dc.date.updated	2024-04-04T07:30:54Z
pubs.publication-status	Published
pubs.volume	133

Abstract:

Overbreak is a detrimental phenomenon caused by tunnel blasting, which can lead to increased time and cost in the construction schedule. It is very important to establish a model that can accurately predict the overbreak caused by tunnel blasting. To achieve this goal, the random forest (RF) is an ensemble machine learning model optimised by three metaheuristic algorithms to predicted overbreak, i.e. the grey wolf optimiser (GWO), the whale optimisation algorithm (WOA), and the tunicate swarm algorithm (TSA). The primary roles of GWO, WOA, and TSA are to search for the optimal hyper-parameters of the RF model in the solution space. To create the models above, 523 data samples were taken from a highway tunnel in China. The established database comprised seven predictors or inputs, including the number of holes, hole depth, total charge, advance length, rock mass rating, tunnel cross-sectional area, and powder factor. Three hybrid RF-based models (RF-GWO, RF-WOA, and RF-TSA) were constructed to predict overbreak. Subsequently, the performance levels of the developed hybrid models were evaluated according to four indices: the coefficient of determination, the root mean square error, the variance accounted for, and the A-20 index. The results showed that the TSA optimisation algorithm was better than the other two algorithms (WOA and GWO) at finding the best hyper-parameters for the RF model. Moreover, the results of comparative analysis with the single RF model confirmed that the proposed RF-TSA model is a strong solution with high accuracy for tackling the overbreak issues. The results of this study showed that the developed models can provide more accurate overbreak values compared to the intelligent techniques available in the literature; they can be used in practice and similar projects.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/177474