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

• Published in: Mathematics (Basel) Vol. 11; no. 6; p. 1305
• Main Authors: Ali, Muhammad, Khan, Naseer Muhammad, Gao, Qiangqiang, Cao, Kewang, Jahed Armaghani, Danial, Alarifi, Saad S., Rehman, Hafeezur, Jiskani, Izhar Mithal
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-03-2023
• Subjects: Acoustic emission; Acoustic emission testing; Acoustics; Algorithms; Artificial intelligence; Artificial neural networks; Coal; Crack closure; Crack initiation; Crack propagation; Cracks; Data collection; Deformation; Dilatancy; Early warning systems; Elastic deformation; Energy; Engineering; Laboratories; Mathematics; Mechanical properties; Methods; Modulus of elasticity; Moisture content; Sensors; Software; Stiffness; Strain energy; Stress propagation; Testing; uniaxial loading; water content; China
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math11061305

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.