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With the increase of mined-out areas and geological disasters, it is necessary to study the influence range of mining. Moreover, with the growth in the amount of data, machine learning methods are commonly used to predict the surface subsidence. The 10 mm subsidence boundary is an important indicator to determine the influence range, and FLAC3D is suitable for solving the nonlinear large deformation problems. Therefore, it can be used to explore the relationship between the influence range of the surface movement (L) and geological mining conditions. Taking working faces A and B of two mines, through the establishment of a numerical model, the relationship between L and bedrock thickness, loose layer thickness, working face mining length, and overlying rock lithology is explored by using the control variable method. Moreover, the variation law is analyzed theoretically and mechanically. The results show that L is positively correlated with the thickness, while negatively correlated with the mining length. Similarly, L shows a slight increase after the key layer becomes weak. This study provides a scientific basis for reasonable coal mining and protection of surface buildings in the mining area. Integrating machine learning into the numerical model further increases the model throughput. The findings show that machine learning models had high accuracy in predicting surface subsidence, with the random forest trees and regression models having lower time and high accuracy, respectively.

Relationship between Surface Subsidence Range and Geological Mining Conditions Using Numerical Simulation and Machine Learning