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

Muhammad Ali, Naseer Muhammad Khan (), Qiangqiang Gao, Kewang Cao (), Danial Jahed Armaghani, Saad S. Alarifi, Hafeezur Rehman and Izhar Mithal Jiskani
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Muhammad Ali: School of Art, Anhui University of Finance and Economics, Bengbu 233030, China
Naseer Muhammad Khan: Department of Sustainable Advanced Geomechanical Engineering, Military College of Engineering, National University of Sciences and Technology, Risalpur 23200, Pakistan
Qiangqiang Gao: Key Laboratory of Deep Coal Resource Mining (China University of Mining & Technology), Ministry of Education, Xuzhou 221116, China
Kewang Cao: School of Art, Anhui University of Finance and Economics, Bengbu 233030, China
Danial Jahed Armaghani: School of Civil and Environmental Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia
Saad S. Alarifi: Department of Geology and Geophysics, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
Hafeezur Rehman: Department of Mining Engineering, Balochistan University of Information Technology, Engineering and Management Sciences (BUITEMS), Quetta 87300, Pakistan
Izhar Mithal Jiskani: Department of Mining and Mineral Resources, National University of Sciences & Technology, Balochistan Campus, Quetta 87300, Pakistan

Mathematics, 2023, vol. 11, issue 6, 1-25

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.

Keywords: acoustic emission; strain energy; water content; artificial intelligence; uniaxial loading (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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