Five multiparameter empirical criteria were exclusively evaluated by comparing them with the strength data covering various stress conditions to find out which failure criterion best fits the test data and describes t...Five multiparameter empirical criteria were exclusively evaluated by comparing them with the strength data covering various stress conditions to find out which failure criterion best fits the test data and describes the mechanical behavior of the salt rock sequence (halite,bedded composite specimens and anhydrite interlayers).Full-scale comparison of all criteria for the three rock types was conducted based on five standard statistics calculated from least squares curve-fitting,which measures both the goodness of fitting and the quality of future prediction.The results indicate that all five nonlinear criteria with a basic power form are efficient in predicting the strength trend in the low tension area as well as in the high compression area of the soft rocks.The parameters obtained for the bedded rock salt are somewhat in the ones for the "pure" rocks and are even closer to those obtained for the halite.The generalized Hoek-Brown criterion is proven to perform best to two rock strength data followed by one for the Bieniawski empirical criterion,thus is the best candidate for the analysis of the salt rock.The Sheorey empirical criterion consistently achieves an intermediate performance for all the three rocks.It seems that the superiority of the poly-axial criteria (the Mogi 1967 criterion and the N-type criterion) over the former three triaxial criteria no longer exists when applied to the conventional triaxial strength data.Besides,the method of tension cut-off was proposed to solve the ambiguity problem of the two poly-axial criteria in the tension field in the plane of the major (σ1) andminor principal stress (σ3).展开更多
Deformation modulus is the important parameter in stability analysis of tunnels, dams and mining struc- tures. In this paper, two predictive models including Mamdani fuzzy system （MFS） and multivariable regression a...Deformation modulus is the important parameter in stability analysis of tunnels, dams and mining struc- tures. In this paper, two predictive models including Mamdani fuzzy system （MFS） and multivariable regression analysis （MVRA） were developed to predict deformation modulus based on data obtained from dilatometer tests carried out in Bakhtiary dam site and additional data collected from longwall coal mines. Models inputs were considered to be rock quality designation, overburden height, weathering, unconfined compressive strength, bedding inclination to core axis, joint roughness coefficient and fill thickness. To control the models performance, calculating indices such as root mean square error （RMSE）, variance account for （VAF） and determination coefficient （R^2） were used. The MFS results show the significant prediction accuracy along with high performance compared to MVRA results. Finally, the sensitivity analysis of MFS results shows that the most and the least effective parameters on deformation modulus are weatherin~ and overburden height, respectively.展开更多
By studying different compressive strengths and changes in the characteristics of rocks,five variables were selected to predict faults in coal mines. Drillholes in the mined area were divided into two populations, i.e...By studying different compressive strengths and changes in the characteristics of rocks,five variables were selected to predict faults in coal mines. Drillholes in the mined area were divided into two populations, i.e., drillholes containing faults and drillholes without faults. Discriminant functions were established from the values of the five variables using Fisher's approach. Drillholes in the non-mined areas were allocated to one of the two populations by using discriminant functions. The terrenes of each drillhole were divided into 10 sections, above and below a minable coal seam. Each section has 10 layers of rocks. The population to which each drillhole in a section belongs is sorted out and the probability of each drillhole with faults obtained,i.e., a contour map of predicting the probability of faults in coal mining is shown. A comparison with the real distribution of faults shows that the precision of accurately predicting faults is greater than 70 per cent.展开更多
基金Project(2009CB724608) supported by the National Basic Research Program of China
文摘Five multiparameter empirical criteria were exclusively evaluated by comparing them with the strength data covering various stress conditions to find out which failure criterion best fits the test data and describes the mechanical behavior of the salt rock sequence (halite,bedded composite specimens and anhydrite interlayers).Full-scale comparison of all criteria for the three rock types was conducted based on five standard statistics calculated from least squares curve-fitting,which measures both the goodness of fitting and the quality of future prediction.The results indicate that all five nonlinear criteria with a basic power form are efficient in predicting the strength trend in the low tension area as well as in the high compression area of the soft rocks.The parameters obtained for the bedded rock salt are somewhat in the ones for the "pure" rocks and are even closer to those obtained for the halite.The generalized Hoek-Brown criterion is proven to perform best to two rock strength data followed by one for the Bieniawski empirical criterion,thus is the best candidate for the analysis of the salt rock.The Sheorey empirical criterion consistently achieves an intermediate performance for all the three rocks.It seems that the superiority of the poly-axial criteria (the Mogi 1967 criterion and the N-type criterion) over the former three triaxial criteria no longer exists when applied to the conventional triaxial strength data.Besides,the method of tension cut-off was proposed to solve the ambiguity problem of the two poly-axial criteria in the tension field in the plane of the major (σ1) andminor principal stress (σ3).
文摘Deformation modulus is the important parameter in stability analysis of tunnels, dams and mining struc- tures. In this paper, two predictive models including Mamdani fuzzy system （MFS） and multivariable regression analysis （MVRA） were developed to predict deformation modulus based on data obtained from dilatometer tests carried out in Bakhtiary dam site and additional data collected from longwall coal mines. Models inputs were considered to be rock quality designation, overburden height, weathering, unconfined compressive strength, bedding inclination to core axis, joint roughness coefficient and fill thickness. To control the models performance, calculating indices such as root mean square error （RMSE）, variance account for （VAF） and determination coefficient （R^2） were used. The MFS results show the significant prediction accuracy along with high performance compared to MVRA results. Finally, the sensitivity analysis of MFS results shows that the most and the least effective parameters on deformation modulus are weatherin~ and overburden height, respectively.
基金Project 40772198 supported by the National Natural Science Foundation of China
文摘By studying different compressive strengths and changes in the characteristics of rocks,five variables were selected to predict faults in coal mines. Drillholes in the mined area were divided into two populations, i.e., drillholes containing faults and drillholes without faults. Discriminant functions were established from the values of the five variables using Fisher's approach. Drillholes in the non-mined areas were allocated to one of the two populations by using discriminant functions. The terrenes of each drillhole were divided into 10 sections, above and below a minable coal seam. Each section has 10 layers of rocks. The population to which each drillhole in a section belongs is sorted out and the probability of each drillhole with faults obtained,i.e., a contour map of predicting the probability of faults in coal mining is shown. A comparison with the real distribution of faults shows that the precision of accurately predicting faults is greater than 70 per cent.
