Monitoring deformations of metro transit tunnels using a neural network approach

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.