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Application of Neural Networks in Rock Mass Stress Assessment by Photoelasticity

  • GEOINFORMATION SCIENCE
  • Published:
Journal of Mining Science Aims and scope

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.

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Authors and Affiliations

  1. Chinakal Institute of Mining, Siberian Branch, Russian Academy of Sciences, 630091, Novosibirsk, Russia

    S. A. Neverov, A. A. Neverov, A. I. Konurin & D. V. Orlov

  2. D. Serikbaev East Kazakhstan Technical University, 070004, Ust-Kamenogorsk, Kazakhstan

    M. A. Adylkanova

Authors
  1. S. A. Neverov
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  2. A. A. Neverov
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  3. A. I. Konurin
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  5. D. V. Orlov
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Correspondence to S. A. Neverov.

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Translated from Fiziko-Tekhnicheskie Problemy Razrabotki Poleznykh Iskopaemykh, 2023, No. 6, pp. 176-189. https://doi.org/10.15372/FTPRPI20230617.

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Neverov, S.A., Neverov, A.A., Konurin, A.I. et al. Application of Neural Networks in Rock Mass Stress Assessment by Photoelasticity. J Min Sci 59, 1045–1057 (2023). https://doi.org/10.1134/S1062739123060170

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  • DOI: https://doi.org/10.1134/S1062739123060170

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