Applications of Soft Computing Methods in Backbreak Assessment in Surface Mines: A Comprehensive Review

Yari, M; Khandelwal, M; Abbasi, P; Koutras, EI; Armaghani, DJ; Asteris, PG

Applications of Soft Computing Methods in Backbreak Assessment in Surface Mines: A Comprehensive Review

Yari, M Khandelwal, M Abbasi, P Koutras, EI Armaghani, DJ Asteris, PG

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Publisher:: TECH SCIENCE PRESS
Publication Type:: Journal Article
Citation:: CMES - Computer Modeling in Engineering and Sciences, 2024, 140, (3), pp. 2207-2238
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Yari, M
dc.contributor.author	Khandelwal, M
dc.contributor.author	Abbasi, P
dc.contributor.author	Koutras, EI
dc.contributor.author	Armaghani, DJ
dc.contributor.author	Asteris, PG
dc.date.accessioned	2025-03-13T23:10:45Z
dc.date.available	2025-03-13T23:10:45Z
dc.date.issued	2024-01-01
dc.identifier.citation	CMES - Computer Modeling in Engineering and Sciences, 2024, 140, (3), pp. 2207-2238
dc.identifier.issn	1526-1492
dc.identifier.issn	1526-1506
dc.identifier.uri	http://hdl.handle.net/10453/185792
dc.description.abstract	Geo-engineering problems are known for their complexity and high uncertainty levels, requiring precise definitions, past experiences, logical reasoning, mathematical analysis, and practical insight to address them effectively. Soft Computing (SC) methods have gained popularity in engineering disciplines such as mining and civil engineering due to computer hardware and machine learning advancements. Unlike traditional hard computing approaches, SC models use soft values and fuzzy sets to navigate uncertain environments. This study focuses on the application of SC methods to predict backbreak, a common issue in blasting operations within mining and civil projects. Backbreak, which refers to the unintended fracturing of rock beyond the desired blast perimeter, can significantly impact project timelines and costs. This study aims to explore how SC methods can be effectively employed to anticipate and mitigate the undesirable consequences of blasting operations, specifically focusing on backbreak prediction. The research explores the complexities of backbreak prediction and highlights the potential benefits of utilizing SC methods to address this challenging issue in geo-engineering projects.
dc.language	English
dc.publisher	TECH SCIENCE PRESS
dc.relation.ispartof	CMES - Computer Modeling in Engineering and Sciences
dc.relation.isbasedon	10.32604/cmes.2024.048071
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	Applications of Soft Computing Methods in Backbreak Assessment in Surface Mines: A Comprehensive Review
dc.type	Journal Article
utslib.citation.volume	140
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
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	2025-03-13T23:10:42Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	140
utslib.citation.issue	3

Abstract:

Geo-engineering problems are known for their complexity and high uncertainty levels, requiring precise definitions, past experiences, logical reasoning, mathematical analysis, and practical insight to address them effectively. Soft Computing (SC) methods have gained popularity in engineering disciplines such as mining and civil engineering due to computer hardware and machine learning advancements. Unlike traditional hard computing approaches, SC models use soft values and fuzzy sets to navigate uncertain environments. This study focuses on the application of SC methods to predict backbreak, a common issue in blasting operations within mining and civil projects. Backbreak, which refers to the unintended fracturing of rock beyond the desired blast perimeter, can significantly impact project timelines and costs. This study aims to explore how SC methods can be effectively employed to anticipate and mitigate the undesirable consequences of blasting operations, specifically focusing on backbreak prediction. The research explores the complexities of backbreak prediction and highlights the potential benefits of utilizing SC methods to address this challenging issue in geo-engineering projects.

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http://hdl.handle.net/10453/185792