Study on large deformation mechanism and control system of soft rock roadway based on Bayesian method

With the increase of the depth and intensity of coal resources mining, a series of new problems have emerged in the maintenance of roadway under soft rock conditions, such as rock properties, in-situ stress types, stope mining, aquifer, roadway shape and size, and the selection of support scheme parameters. Many factors affect the displacement of soft rock, and the distribution law of stress field in roadway surrounding rock has new characteristics. In order to realize the real-time quantitative control of the surrounding rock state of the roadway, it is bound to transmit the roadway monitoring data to the centralized control center in real time through the full section scanner and other sensing equipment, analyze the influencing factors and parameters related to the large deformation of soft rock by using the neural network method, determine the highest weight factor leading to the large deformation in sections of the roadway, and according to the roadway section shape, support parameters The optimal scheme for timely adjustment is given from four aspects: support scheme and mining process parameters. Form a closed-loop electromechanical intelligent system of monitoring sensor - equipment train - centralized control center - Bayesian neural network analysis - scheme customization - strengthening support and controlling surrounding rock. It can provide reference for the study of large deformation mechanism and control technology of soft rock roadway under similar conditions.