Study On Mechanical Properties And Strength Criterion Of Sandstone Under Mining Unloading Condition

The human demand for coal production is increasing,and many coal mines have entered the ranks of deep mining.deep mining is bound to become the most important way to ensure the sustainable development and supply of mineral resources in China in the future.However,deep mining faces many difficulties,and mine disasters are becoming more and more frequent under the harsh environment and high-intensity mining disturbance in the deep part,which poses a serious threat to the safe and efficient mining of deep resources,while the applicability of traditional rock mechanics and mining theory under deep mining conditions is controversial.Underground excavation is an unloading process,and the deformation and damage characteristics of rocks under unloading conditions are very different from those under loading conditions.For this reason,the mechanical properties of rocks under the influence of deep mining unloading are investigated in this paper.The true triaxial loading and unloading tests under different initial minimum principal stress and intermediate principal stress conditions are carried out.The rock strength is predicted by using neural network.The nonlinear Mohr-Coulomb strength criterion is improved by using the initial confining pressure and numerical simulation.The main research results are as follows:(1)The true triaxial loading and unloading tests were carried out on sandstone under different initial minimum principal stresses and intermediate principal stresses using the true triaxial test system,and the variation law of the strength of sandstone under different initial minimum principal stresses and intermediate principal stresses was analyzed.It was found that the peak strength of sandstone under true triaxial conditions increased with the increase of intermediate principal stress;with the increase of minimum principal stress,the peak strength of sandstone with unloading damage gradually increased,and the fitting curve of peak strength and minimum principal stress was approximately straight line.(2)BP neural network was combined with genetic algorithm and particle swarm algorithm to predict the rock strength using evolved neural network.The results show that the established neural network can predict the rock strength well,and the predicted value considering the initial minimum principal stress is closer to the real value measured by the test than the predicted value without considering the initial surrounding pressure.(3)The nonlinear Mohr-Coulomb strength criterion was improved by applying the initial minimum principal stress,and its accuracy and applicability were verified.The results show that the improved strength criterion is more accurate in predicting the results and can accurately predict the test strength of different types of rocks under different surrounding pressures,and has good general applicability.(4)The results show that the maximum principal stress of sandstone unloading damage gradually increases with the increase of intermediate principal stress and minimum principal stress,and the fitted curve of the relationship between the maximum and minimum principal stress is approximately straight line;the results obtained from numerical simulation are consistent with the mechanical experiments in terms of change trend.