To investigate the correlation between the uniaxial compressive strength and elastic modulus of overlying strata sandstone and mudstone with greater accuracy,historical data consisting of 56 sets of uniaxial compressive strength and elastic modulus of sandstone and mudstone from Hujiahe Coal Mine were analyzed.Genetic algorithms were utilized to optimize the structure and learning parameters of a BP neural network,resulting in the identification of the optimal network structure and parameter settings.The GA-BP neural net-work was then applied to predict the uniaxial compressive strength of coal mine sandstone and mudstone.Comparisons were made with traditional BP neural networks and nonlinear regression analysis methods.The research findings indicate that the GA-BP neural network prediction model achieves higher accuracy and generalization capability in predicting the relationship between the uniaxial compressive strength and elastic modulus of sandstone and mudstone.It effectively addresses the local optimum and overfitting issues associated with traditional BP neural networks and exhibits superior nonlinear relationship modeling capabilities compared to nonlinear regression analy-sis.Therefore,it is considered an effective method for predicting the uniaxial compressive strength of sandstone and mudstone.
rock mechanical parametersnonlinear regressionBP neural networkgenetic algorithmprediction model
万方数据
维普
