Monitoring deformations of metro transit tunnels using a neural network approach

DOI: https://doi.org/10.30686/1609-9192-2025-2-163-166

Читать на русскоя языкеA.D. Meller1, R.R. Galiyeva2, A.V. Kuleshova2, A.K. Petrosyan2, S.A. Glatko2
1 RUDN University, Moscow, Russian Federation
2 National University of Science and Technology MISIS, Moscow, Russian Federation

Russian Mining Industry №2 / 2025 p.163-166

Abstract: The article discusses application of artificial neural networks and machine learning algorithms for monitoring of deformations in subway tunnels located in complex mining and geological conditions. Particular attention is given to industrial and environmental safety, as well as modern methods for measuring crustal deformations using GPS/GLONASS technologies, geodetic, and mine surveying. The main stages of artificial neural networks operation are described, i.e. the training based on the tunnel parameters and conditions, testing, validation, and operation to predict the potential deformations. The key neural network architectures are considered such as the deep, convolutional, and recurrent networks along with their data processing capabilities. Examples are provided of artificial neural networks used for data interpolation, hazardous zone recognition, and tunnel ring monitoring. The importance of high-quality initial data, including geometric parameters, physical material properties, climatic conditions, and historical data, is emphasized. Implementation of artificial neural networks can help to promptly identify risks, predict the deformation dynamics, and classify the deformation types, enabling timely measures to prevent emergencies.

Keywords: artificial neural networks, tunnel deformations, structural monitoring, machine learning, geodynamics, data interpolation

For citation: Meller A.D., Galiyeva R.R., Kuleshova A.V., Petrosyan A.K., Glatko S.A. Monitoring deformations of metro transit tunnels using a neural network approach. Russian Mining Industry. 2025;(2):163–166. (In Russ.) https://doi.org/10.30686/1609-9192-2025-2-163-166


Article info

Received: 06.01.2025

Revised: 05.03.2025

Accepted: 17.03.2025


Information about the authors

Alexander D. Meller – Postgraduate Student, Department of Subsoil Use and Oil and Gas Engineering, RUDN University, Moscow, Russian Federation; e-mail: mellera33@gmail.com

Rita R. Galiyeva – Postgraduate Student, Department of Energy-Efficient and Resource-Saving Industrial Technologies, National University of Science and Technology MISIS, Moscow, Russian Federation; e-mail: jebulcan@gmail.com

Anastasia V. Kuleshova – Postgraduate Student, Department of Energy-Efficient and Resource-Saving Industrial Technologies, National University of Science and Technology MISIS, Moscow, Russian Federation; e-mail: nastya.kramar98@gmail.com

Artur K. Petrosyan – Postgraduate Student, Department of Energy-Efficient and Resource-Saving Industrial Technologies, National University of Science and Technology MISIS, Moscow, Russian Federation; e-mail: petrosyan98.archi@yandex.ru

Svetlana A. Glatko – Postgraduate Student, Department of Geology and Surveying, College of Mining, National University of Science and Technology MISIS, Moscow, Russian Federation; e-mail: taratorina.svetlana99@mail.ru


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