Abstract
Considering the shortcomings of the currently used time functions for dynamically predicting surface mining subsidence and calculating its parameters, a novel time function is proposed on the basis of an in-depth analysis on the movement characteristics of mining surface points in a fully mined area and the measured mining subsidence data in the field during the course of the mining process. The proposed function can be used to effectively characterize the surface subsidence, the subsidence velocity, and the acceleration of the mining area. All the parameters involved in the function have their physical meaning, and their influence on the function was also analyzed in this study. A parameter calculation method is proposed for the new time function based on the normalization method and least square principle. Taking the measured dynamic subsidence data of 22,618 working faces in a coal mine as an example, the reliability of the new time function model was verified by comparing the measured data with the predicted results. The results show that the average relative root-mean-square error was 5.2%, and the prediction accuracy was improved compared with the Knothe time function, double-parameter Knothe time function, and piecewise optimized Knothe time function.