Optimizing fracture toughness estimation for rock structures: A soft computing approach with GWO and IWO algorithms

Fattahi, H; Ghaedi, H; Armaghani, DJ

Optimizing fracture toughness estimation for rock structures: A soft computing approach with GWO and IWO algorithms

Fattahi, H Ghaedi, H Armaghani, DJ

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Measurement: Journal of the International Measurement Confederation, 2024, 238, pp. 115306
Issue Date:: 2024-10-01

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Field	Value	Language
dc.contributor.author	Fattahi, H
dc.contributor.author	Ghaedi, H
dc.contributor.author	Armaghani, DJ
dc.date.accessioned	2025-01-15T05:45:05Z
dc.date.available	2025-01-15T05:45:05Z
dc.date.issued	2024-10-01
dc.identifier.citation	Measurement: Journal of the International Measurement Confederation, 2024, 238, pp. 115306
dc.identifier.issn	0263-2241
dc.identifier.uri	http://hdl.handle.net/10453/183638
dc.description.abstract	The importance of mode I fracture toughness (KIc) lies in its role in studying rock fractures, finding application across diverse sectors like mining, tunneling, and surface construction. Given the time-consuming, costly, and arduous nature of determining fracture toughness through lab and field tests, employing indirect techniques is advised. Soft computing's indirect methods excel at modeling intricate nonlinear connections with optimal efficiency and accuracy, even when geological parameters introduce uncertainty. The innovative aspect of this research lies in leveraging Grey Wolf Optimization (GWO) and Invasive Weed Optimization (IWO) algorithms to model 88 Chevron Notched Brazilian Disc (CCNBD) specimens, designated by ISRM, considering uncertainties. The input parameters encompass uniaxial tensile strength (σt), initial crack length (α0), disc specimen radius (R), crack length (αB), disc thickness (B), and final crack length (α1). The research proceeds by employing 80% of the dataset (70 data points) for constructing models via the algorithms, reserving the remaining 20% (18 data points) for model validation. The outcomes, evaluated using statistical metrics such as mean square error (MSE), squared correlation coefficient (R2), mean absolute error (MAE), and root mean square error (RMSE) underscore the exceptional accuracy and performance of GWO and IWO algorithms in predicting and estimating rock structure's KIc. Moreover, this paper conducts a sensitivity analysis on KIc estimation's input parameters using @RISK software. Findings reveal that among the input parameters, σt exerts the most substantial influence on the model's output. Consequently, even a minor alteration in σt yields significant fluctuations in the relationship's output.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Measurement: Journal of the International Measurement Confederation
dc.relation.isbasedon	10.1016/j.measurement.2024.115306
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0913 Mechanical Engineering
dc.subject.classification	Electrical & Electronic Engineering
dc.subject.classification	46 Information and computing sciences
dc.subject.classification	49 Mathematical sciences
dc.title	Optimizing fracture toughness estimation for rock structures: A soft computing approach with GWO and IWO algorithms
dc.type	Journal Article
utslib.citation.volume	238
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0913 Mechanical 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	2025-01-15T05:45:01Z
pubs.publication-status	Published
pubs.volume	238

Abstract:

The importance of mode I fracture toughness (KIc) lies in its role in studying rock fractures, finding application across diverse sectors like mining, tunneling, and surface construction. Given the time-consuming, costly, and arduous nature of determining fracture toughness through lab and field tests, employing indirect techniques is advised. Soft computing's indirect methods excel at modeling intricate nonlinear connections with optimal efficiency and accuracy, even when geological parameters introduce uncertainty. The innovative aspect of this research lies in leveraging Grey Wolf Optimization (GWO) and Invasive Weed Optimization (IWO) algorithms to model 88 Chevron Notched Brazilian Disc (CCNBD) specimens, designated by ISRM, considering uncertainties. The input parameters encompass uniaxial tensile strength (σt), initial crack length (α0), disc specimen radius (R), crack length (αB), disc thickness (B), and final crack length (α1). The research proceeds by employing 80% of the dataset (70 data points) for constructing models via the algorithms, reserving the remaining 20% (18 data points) for model validation. The outcomes, evaluated using statistical metrics such as mean square error (MSE), squared correlation coefficient (R2), mean absolute error (MAE), and root mean square error (RMSE) underscore the exceptional accuracy and performance of GWO and IWO algorithms in predicting and estimating rock structure's KIc. Moreover, this paper conducts a sensitivity analysis on KIc estimation's input parameters using @RISK software. Findings reveal that among the input parameters, σt exerts the most substantial influence on the model's output. Consequently, even a minor alteration in σt yields significant fluctuations in the relationship's output.

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