251 / 2023-09-30 02:31:41

A Method for Predicting Subsidence in Thick Loose Layer Mines Based on Segmental Weighted Parameter Assignment

thick loose layer,probabilistic integral method,parameter inversion,mining subsidence,divisional segment forecast

As a generalized subsidence prediction method for coal mining, the probability integral method has the problems of too fast edge convergence and inaccurate subsidence prediction in the application of thick loose bed mining area, which brings certain difficulties to the "three downs" coal mining and environmental protection in thick loose bed mining area. Based on the probability integral method, this paper proposes a subsidence estimation method for thick loose layer mining area based on segmented weighted assignment, dividing the mining area into inner and outer segments according to the location, obtaining the corresponding estimation parameters, establishing two adaptive assignment function models to weight the combination of the estimation results, and establishing a partitioned estimation parameter inversion method based on the standard particle swarm algorithm and the adaptive simulated annealing particle swarm algorithm. Experimental validation shows that: the expected subsidence value of this method is closer to the measured data, and the residual sum of squares of the two assignment models are 0.0071m^2 and 0.0058m^2, which are smaller than 0.0203m^2 of the original method; compared with the two kinds of assignment function models, the model two is more accurate and more effective; under the same conditions, the accuracy of the Adaptive Simulated Annealing Particle Swarm Algorithm is higher than that of the Standard Particle Swarm Algorithm. This paper verifies the feasibility of the mining area zoning section prediction, confirms that the method can effectively solve the problem that the edge convergence is too fast in the application of probability integral method in the thick loose layer mining area, and has certain guiding significance to the subsidence prediction of the thick loose layer mining area.