Application of Neural Networks in Rock Mass Stress Assessment by Photoelasticity
Neverov S.A. Neverov A.A. Konurin A.I. Adylkanova M.A. Orlov D.V.
December 2023Pleiades Publishing
Journal of Mining Science
2023#59Issue 61045 - 1057 pp.
Abstract: The optical polarization method with ring-shaped photoelastic sensors, digital photography of isochromatic patterns and their clarification using neural networks is developed for the stress measurement in rock mass. The case-studies of the photoelasticity application in solving various problems of elasticity and rock pressure analysis are reviewed. As a result of a lab-scale experiment, a data set of 15000 isochromatic images is collected. The machine learning algorithm was a convolutional neural network, the Inception module. The authors recommend using downhole sensors for the continuous stress monitoring in underground mines and integrating the obtained data in a digital model with the help of IoT.
borehole , contour lines , experiment , geomechanical data , isochromatic curves , modeling , neural networks , optical pattern , photoelasticity , rock mass , sensor , stress–strain behavior
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Chinakal Institute of Mining, Siberian Branch, Russian Academy of Sciences, Novosibirsk, 630091, Russian Federation
D. Serikbaev East Kazakhstan Technical University, Ust-Kamenogorsk, 070004, Kazakhstan
Chinakal Institute of Mining
D. Serikbaev East Kazakhstan Technical University
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