Prediction of Coal Dilatancy Point Using Acoustic Emission Characteristics: Insight Experimental and Artificial Intelligence Approaches

Ali, M; Khan, NM; Gao, Q; Cao, K; Jahed Armaghani, D; Alarifi, SS; Rehman, H; Jiskani, IM

Prediction of Coal Dilatancy Point Using Acoustic Emission Characteristics: Insight Experimental and Artificial Intelligence Approaches

Ali, M Khan, NM Gao, Q Cao, K Jahed Armaghani, D

Alarifi, SS Rehman, H Jiskani, IM

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Mathematics, 2023, 11, (6)
Issue Date:: 2023-03-01

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Field	Value	Language
dc.contributor.author	Ali, M
dc.contributor.author	Khan, NM
dc.contributor.author	Gao, Q
dc.contributor.author	Cao, K
dc.contributor.author	Jahed Armaghani, D https://orcid.org/0000-0001-8171-6403
dc.contributor.author	Alarifi, SS
dc.contributor.author	Rehman, H
dc.contributor.author	Jiskani, IM
dc.date.accessioned	2024-04-24T05:09:07Z
dc.date.available	2024-04-24T05:09:07Z
dc.date.issued	2023-03-01
dc.identifier.citation	Mathematics, 2023, 11, (6)
dc.identifier.issn	2227-7390
dc.identifier.issn	2227-7390
dc.identifier.uri	http://hdl.handle.net/10453/178315
dc.description.abstract	This research offers a combination of experimental and artificial approaches to estimate the dilatancy point under different coal conditions and develop an early warning system. The effect of water content on dilatancy point was investigated under uniaxial loading in three distinct states of coal: dry, natural, and water-saturated. Results showed that the stiffness-stress curve of coal in different states was affected differently at various stages of the process. Crack closure stages and the propagation of unstable cracks were accelerated by water. However, the water slowed the elastic deformation and the propagation of stable cracks. The peak strength, dilatancy stress, elastic modulus, and peak stress of natural and water-saturated coal were less than those of dry. An index that determines the dilatancy point was derived from the absolute strain energy rate. It was discovered that the crack initiation point and dilatancy point decreased with the increase in acoustic emission (AE) count. AE counts were utilized in artificial neural networks, random forest, and k-nearest neighbor approaches for predicting the dilatancy point. A comparison of the evaluation index revealed that artificial neural networks prediction was superior to others. The findings of this study may be valuable for predicting early failures in rock engineering.
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	Mathematics
dc.relation.isbasedon	10.3390/math11061305
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	49 Mathematical sciences
dc.title	Prediction of Coal Dilatancy Point Using Acoustic Emission Characteristics: Insight Experimental and Artificial Intelligence Approaches
dc.type	Journal Article
utslib.citation.volume	11
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.date.updated	2024-04-24T05:08:53Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	11
utslib.citation.issue	6

Abstract:

This research offers a combination of experimental and artificial approaches to estimate the dilatancy point under different coal conditions and develop an early warning system. The effect of water content on dilatancy point was investigated under uniaxial loading in three distinct states of coal: dry, natural, and water-saturated. Results showed that the stiffness-stress curve of coal in different states was affected differently at various stages of the process. Crack closure stages and the propagation of unstable cracks were accelerated by water. However, the water slowed the elastic deformation and the propagation of stable cracks. The peak strength, dilatancy stress, elastic modulus, and peak stress of natural and water-saturated coal were less than those of dry. An index that determines the dilatancy point was derived from the absolute strain energy rate. It was discovered that the crack initiation point and dilatancy point decreased with the increase in acoustic emission (AE) count. AE counts were utilized in artificial neural networks, random forest, and k-nearest neighbor approaches for predicting the dilatancy point. A comparison of the evaluation index revealed that artificial neural networks prediction was superior to others. The findings of this study may be valuable for predicting early failures in rock engineering.

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http://hdl.handle.net/10453/178315