RESEARCH ON APPROACHES TO ASSESSING AND FORECASTING GEOTECHNICAL RISKS

Authors

D.A. Karimova Institute of Mechanical Engineering, Automation and Geomechanics of the National Academy of Sciences of the Kyrgyz Republic

Keywords:

геотехнический мониторинг, машинное обучение.

Abstract

The article is devoted to the application of machine learning to improve geotechnical monitoring methods. The application of machine learning (ML) methods in the assessment of risks associated with the safety of infrastructure projects in natural and man-made conditions is considered. The stages of ML application are described, including data collection and processing, selection of algorithms, training and validation of models. The advantages and limitations of traditional and modern methods are discussed, cases of successful ML implementation in various geotechnical projects are presented, as well as examples of use in various conditions.

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