The joint roughness coefficient(JRC)is a key parameter in the assessment of mechanical properties and the stability of rock masses.This paper presents a novel approach to JRC evaluation using a genetic algorithm-optim...The joint roughness coefficient(JRC)is a key parameter in the assessment of mechanical properties and the stability of rock masses.This paper presents a novel approach to JRC evaluation using a genetic algorithm-optimized backpropagation(GA-BP)neural network.Conventional JRC evaluations have typically depended on two-dimensional(2D)and three-dimensional(3D)parameter calculation methods,which fail to fully capture the nonlinear relationship between the complex surface morphology of joints and their roughness.Our analysis from shear tests on eight different joint types revealed that the strength and failure characteristics of the joints not only exhibit directional dependence but also positively correlate with surface dip angles,heights,and back slope morphological features.Subsequently,five simple statistical parameters,i.e.average dip angle,median dip angle,average height,height coefficient of variation,and back slope feature value(K),were utilized to quantify these characteristics.For the prediction of JRC,we compiled and analyzed 105 datasets,each containing these five statistical parameters and their corresponding JRC values.A GA-BP neural network model was then constructed using this dataset,with the five morphological characteristic statistics serving as inputs and the JRC values as outputs.A comparative analysis was performed between the GA-BP neural network model,the statistical parameter method,and the fractal parameter method.This analysis confirmed that our proposed method offers higher accuracy in evaluating the roughness coefficient and shear strength of joints.展开更多
Pollution flashover accidents occur frequently in railway OCS in saline-alkali areas.To accurately predict the pollution flashover voltage of insulators,a pollution flashover warning should be made in advance.Accordin...Pollution flashover accidents occur frequently in railway OCS in saline-alkali areas.To accurately predict the pollution flashover voltage of insulators,a pollution flashover warning should be made in advance.According to the operating environment of insulators along the Qinghai-Tibet railway,the pollution flashover experiments were designed for the cantilever composite insulator FQBG-25/12.Through the experiments,the flashover voltage under the influence of soluble contaminant density(SCD)of different pollution components,non-soluble deposit density(NSDD),temperature(T),and atmospheric pressure(P)was obtained.On this basis,the GA-BP neural network prediction model was established.P,SCD,NSDD,CaSO_(4) mass fraction(w(CaSO_(4))),and T were taken as input parameters,50%flashover voltage(U_(50%))of the insulator was taken as output parameters.The results showed that the prediction deviation was less than 10%,which meets the basic engineering requirements.The results could not only provide early warning for the anti-pollution flashover work of the railway power supply department,but also be used as an auxiliary contrast to verify the accuracy of the results of the experiments,and provide a theoretical basis for the classification of pollution levels in different regions.展开更多
基金funded by the National Natural Science Foundation of China(Grant Nos.42472345,52325905,42407202).
文摘The joint roughness coefficient(JRC)is a key parameter in the assessment of mechanical properties and the stability of rock masses.This paper presents a novel approach to JRC evaluation using a genetic algorithm-optimized backpropagation(GA-BP)neural network.Conventional JRC evaluations have typically depended on two-dimensional(2D)and three-dimensional(3D)parameter calculation methods,which fail to fully capture the nonlinear relationship between the complex surface morphology of joints and their roughness.Our analysis from shear tests on eight different joint types revealed that the strength and failure characteristics of the joints not only exhibit directional dependence but also positively correlate with surface dip angles,heights,and back slope morphological features.Subsequently,five simple statistical parameters,i.e.average dip angle,median dip angle,average height,height coefficient of variation,and back slope feature value(K),were utilized to quantify these characteristics.For the prediction of JRC,we compiled and analyzed 105 datasets,each containing these five statistical parameters and their corresponding JRC values.A GA-BP neural network model was then constructed using this dataset,with the five morphological characteristic statistics serving as inputs and the JRC values as outputs.A comparative analysis was performed between the GA-BP neural network model,the statistical parameter method,and the fractal parameter method.This analysis confirmed that our proposed method offers higher accuracy in evaluating the roughness coefficient and shear strength of joints.
基金Supported by the National Natural Science Foundation of China(51767014)the Scientific and Technological Research and Development Program of the China Railway(2017J010-C/2017).
文摘Pollution flashover accidents occur frequently in railway OCS in saline-alkali areas.To accurately predict the pollution flashover voltage of insulators,a pollution flashover warning should be made in advance.According to the operating environment of insulators along the Qinghai-Tibet railway,the pollution flashover experiments were designed for the cantilever composite insulator FQBG-25/12.Through the experiments,the flashover voltage under the influence of soluble contaminant density(SCD)of different pollution components,non-soluble deposit density(NSDD),temperature(T),and atmospheric pressure(P)was obtained.On this basis,the GA-BP neural network prediction model was established.P,SCD,NSDD,CaSO_(4) mass fraction(w(CaSO_(4))),and T were taken as input parameters,50%flashover voltage(U_(50%))of the insulator was taken as output parameters.The results showed that the prediction deviation was less than 10%,which meets the basic engineering requirements.The results could not only provide early warning for the anti-pollution flashover work of the railway power supply department,but also be used as an auxiliary contrast to verify the accuracy of the results of the experiments,and provide a theoretical basis for the classification of pollution levels in different regions.
