This article proposes an approach to the organization of the monitoring of geotechnical systems, which allows to increase the effectiveness of the automated control of destructive processes in geotechnical systems. The effectiveness of control is achieved by optimizing economic costs and technical constraints. The technical parameters of an automated geotechnical monitoring system are determined by the cost and importance of controlled objects. The criterion of optimality is the criterion of minimizing costs when implementing a geotechnical monitoring system with a constant value of possible damage. Possible damage in the geotechnical system during the development of destructive processes is expressed in monetary terms. The costs of implementing a geotechnical monitoring system are determined based on its cost, fixed costs for its maintenance, as well as technical parameters. The technical parameters in assessing the cost of implementing a geotechnical monitoring system are: the cost of measuring one controlled parameter, the number of measurement points, the cost of skipping the development of destructive geotechnical processes, the cost of a false alarm system. The proposed approach to optimizing the cost of implementing a geotechnical monitoring system is based on the monitoring method at key control points with a choice of bifurcation parameters. The proposed approach for optimizing the geotechnical monitoring system was tested in the city of Murom, Vladimir Region, and the Russian Federation while observing the development of suffusion processes of technogenic origin. The results of practical testing showed the possibility of reducing the cost of monitoring organization while maintaining control accuracy.

The article discusses the algorithm for assessing changes in the level of risk of karst hazard of the territory based on the forecasting of the number of holes depending on the water level. Based on the results of the analysis, the values of bifurcation parameters are determined, the transition through which sharply increases the formation of new holes. The article provides the developed block diagram of the neural network for assessing the dynamics of the occurrence of holes, as well as an algorithm for generating a predictive estimate of the number of holes. An analysis of the occurrence of the number of holes is carried out on the basis of water level data in the Oka River. The results of spline interpolation of the data are presented in the dependence on the number of holes on the dynamics of the water level in the river for the current and previous year. Practical verification of the developed algorithm was carried out on the basis of the new set of data on the water level in the river and the number of holes. The developed algorithm can be used in predicting the spread (leaching from the soil) of pollutants.

Introduction. During the operation of urban and rural geotechnical systems, the mechanical indicators of the stability of the soil base are significantly influenced by the regime and dynamics of the physicochemical properties of nearby water bodies, caused by the development of karst-suffusion processes. The results of information analysis of data on the dynamics of the river runoff level and water salinity make it possible to increase the accuracy of predictive estimates of the loss of stability of the geotechnical system due to the development of karst-suffusion processes. Goals and objectives. The aim of the work is to improve the safety of operation of geotechnical systems and increase the efficiency of modeling and forecasting systems for geodynamics by developing an algorithm for assessing changes in the risk of developing suffusion processes based on an analysis of the dynamics of the level of groundwater and surface waters. Methods. The paper analyzes the data on the number of karst sinkholes depending on the dynamics of the river water level, obtained on the basis of statistical sources and reports on regime observations, as well as a result of field research. In the course of data processing, a spline interpolation method was used, an algorithm and a neural network for predicting a failure using the Bayesian regularization method based on a network training function was developed, which updates the weight and bias of the value in accordance with the Levenberg-Marquardt optimization. Results and its discussion. Based on the results of practical use, the effectiveness of the developed algorithm was confirmed in identifying the dynamics of the formation of dips during information processing of data on changes in the water level in the Oka River and data on the appearance of new dips in the period from 2012 to 2019. Conclusion. The results obtained in the work make it possible to judge the presence of the expediency of using the developed algorithm for assessing the occurrence of karst sinkholes when monitoring the stability of geotechnical systems and assessing the safety of their operation in general. Prospects for further work are associated with expanding the set of training data and adapting the structure of the neural network to the individual characteristics of the territories by introducing additional geological, hydrological and climatic parameters into the processing. Key words: geotechnical system, stability, karst sinkhole, bifurcation parameters, information processing of data.

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