For the investigation of mechanical properties of the bimrocks with high rock block proportion,a series of laboratory experiments,including resonance frequency and uniaxial compressive tests,are conducted on the 64 fa...For the investigation of mechanical properties of the bimrocks with high rock block proportion,a series of laboratory experiments,including resonance frequency and uniaxial compressive tests,are conducted on the 64 fabricated bimrocks specimens.The results demonstrate that dynamic elastic modulus is strongly correlated with the uniaxial compressive strength,elastic modulus and block proportions of the bimrocks.In addition,the density of the bimrocks has a good correlation with the mechanical properties of cases with varying block proportions.Thus,three crucial indices(including matrix strength)are used as basic input parameters for the prediction of the mechanical properties of the bimrocks.Other than adopting the traditional simple regression and multi-regression analyses,a new prediction model based on the optimized general regression neural network(GRNN)algorithm is proposed.Note that,the performance of the multi-regression prediction model is better than that of the simple regression model,owing to the consideration of various influencing factors.However,the comparison between model predictions indicates that the optimized GRNN model performs better than the multi-regression model does.Model validation and verification based on fabricated data and experimental data from the literature are performed to verify the predictability and applicability of the proposed optimized GRNN model.展开更多
基金Projects(51978669,U1734208)supported by the National Natural Science Foundation of ChinaProject(2018JJ3657)supported by Natural Science Foundation of Hunan Province,China
文摘For the investigation of mechanical properties of the bimrocks with high rock block proportion,a series of laboratory experiments,including resonance frequency and uniaxial compressive tests,are conducted on the 64 fabricated bimrocks specimens.The results demonstrate that dynamic elastic modulus is strongly correlated with the uniaxial compressive strength,elastic modulus and block proportions of the bimrocks.In addition,the density of the bimrocks has a good correlation with the mechanical properties of cases with varying block proportions.Thus,three crucial indices(including matrix strength)are used as basic input parameters for the prediction of the mechanical properties of the bimrocks.Other than adopting the traditional simple regression and multi-regression analyses,a new prediction model based on the optimized general regression neural network(GRNN)algorithm is proposed.Note that,the performance of the multi-regression prediction model is better than that of the simple regression model,owing to the consideration of various influencing factors.However,the comparison between model predictions indicates that the optimized GRNN model performs better than the multi-regression model does.Model validation and verification based on fabricated data and experimental data from the literature are performed to verify the predictability and applicability of the proposed optimized GRNN model.
