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

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: Mathematics

سال: 2023

ISSN: ['2227-7390']

DOI: https://doi.org/10.3390/math11061305