Research on deformation prediction method for mining-induced overburden in coal mines based on BP neural network

Coal mining triggers the initiation, propagation, and coalescence of fractures in overburden strata, which can readily induce geological disasters such as mine water inrush and surface subsidence. This study employed distributed optical fiber sensing (DOFS) technology to capture strain distribution curves in overburden strata during coal extraction via physical similarity modeling of Longwall Face 22,107 at Jinfeng Cuncaota Coal Mine. Based on the geological conditions of the overburden and coal mining parameters, seven key factors influencing mining-induced deformation were identified. A neural network architecture was constructed to establish a prediction model for overburden deformation states using measured strain data. Results indicate that the distribution of the caved and fractured zones is closely related to the positions of the main roof and key strata. Predictions from the backpropagation neural network (BPNN) align well with measured strain values, achieving a coefficient of determination (R 2 ) greater than 0.9 and a root mean square error (RMSE) below 10%, demonstrating high applicability. These findings validate the feasibility of integrating DOFS with BPNN for predicting mining-induced overburden deformation.