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Application of artificial neural networks for stress state analysis based on the photoelastic method

Geohazard Mechanics 2023年第2期1卷 128-139页

作者： Anton Konurin Neverov Sergey Neverov Alexandr Orlov Dmitry Zharov Ivan konurina maria N.A.Chinakal Institute of Mining Siberian BranchRussian Academy of Sciences630091Russia Novosibirsk State Technical University 630090Russia National University of Science and Technology“MISIS” 119049Russia

The present article proposes an evolutionary development of the photoelasticity method for measuring stresses based on annular photoelastic sensors application along with stress pattern recording with the aid of a digital camera and its recognition using artificial neural *** analysis of the modern application of the photoelasticity method for various problems within the theory of strength is *** principle of operation of photoelastic sensors based on the photoelasticity effect is *** patterns in an annular photoelastic sensor are presented for various values of the horizontal *** calculation of the stress state of the sensor for the following full-scale experiment has been performed,the estimate of the threshold conditions under which the sensor can be applied has been *** a result of a laboratory experiment,a dataset of 1500 isochromatic images has been assembled.A subspecies of a neural network,namely a convolutional neural network,has been applied as a machine learning *** combination of models and optimizers have been *** application of downhole sensors for continuous monitoring of alterations in the rock mass stress state and the integration of this data into a digital field model based on Internet of Things technologies has been proposed.

关键词： Artificial neural networks Convolutional neural network Photoelasticity Polariscope

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