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

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  • Muhammad Ali

    (School of Art, Anhui University of Finance and Economics, Bengbu 233030, China
    School of Safety Engineering, China University of Mining and Technology, Xuzhou 221116, China
    Department of Mining Engineering, Balochistan University of Information Technology, Engineering and Management Sciences (BUITEMS), Quetta 87300, Pakistan
    Key Laboratory of Deep Coal Resource Mining (China University of Mining & Technology), Ministry of Education, Xuzhou 221116, 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
    School of Materials and Mineral Resources Engineering, University Sains Malaysia, Engineering Campus, Nibong Tebal 14300, Penang, Malaysia)

  • Izhar Mithal Jiskani

    (Department of Mining and Mineral Resources, National University of Sciences & Technology, Balochistan Campus, Quetta 87300, Pakistan)

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.

Suggested Citation

  • Muhammad Ali & Naseer Muhammad Khan & Qiangqiang Gao & Kewang Cao & Danial Jahed Armaghani & Saad S. Alarifi & Hafeezur Rehman & Izhar Mithal Jiskani, 2023. "Prediction of Coal Dilatancy Point Using Acoustic Emission Characteristics: Insight Experimental and Artificial Intelligence Approaches," Mathematics, MDPI, vol. 11(6), pages 1-25, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1305-:d:1091362
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    References listed on IDEAS

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    Cited by:

    1. Pengxiang Zhao & Jian Wen & Shugang Li & Weidong Lu & Yongchen He & Fang Lou & Laolao Wang, 2024. "Characterization of the Time–Space Evolution of Acoustic Emissions from a Coal-like Material Composite Model and an Analysis of the Effect of the Dip Angle on the Bursting Tendency," Sustainability, MDPI, vol. 16(5), pages 1-14, February.
    2. Haojun Xia & Huimei Zhang & Jiafan Zhang, 2023. "Research on Damage Mechanism and Mechanical Characteristics of Coal Rock under Water Immersion," Sustainability, MDPI, vol. 15(17), pages 1-18, August.
    3. Hui Liu & Jianxi Ren & Xinyue Dai & Can Mei & Di Wang & Runqi Wang & Minkai Zhu, 2023. "Evolution Law of Acoustic–Thermal Effect of Freeze–Thaw Sandstone Failure Based on Coupling of Multivariate Monitoring Information," Sustainability, MDPI, vol. 15(12), pages 1-22, June.

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