The quantitative evaluation of errors involved in a particular numerical modelling is of prime importance for the effectiveness and reliability of the method. Errors in Distinct Element Modelling are generated mainly ...The quantitative evaluation of errors involved in a particular numerical modelling is of prime importance for the effectiveness and reliability of the method. Errors in Distinct Element Modelling are generated mainly through three resources as simplification of physical model, determination of parameters and boundary conditions. A measure of errors which represent the degree of numerical solution 'close to true value' is proposed through fuzzy probability in this paper. The main objective of this paper is to estimate the reliability of Distinct Element Method in rock engineering practice by varying the parameters and boundary conditions. The accumulation laws of standard errors induced by improper determination of parameters and boundary conditions are discussed in delails. Furthermore, numerical experiments are given to illustrate the estimation of fuzzy reliability. Example shows that fuzzy reliability falls between 75%-98% when the relative standard errors of input data is under 10 %.展开更多
文摘The quantitative evaluation of errors involved in a particular numerical modelling is of prime importance for the effectiveness and reliability of the method. Errors in Distinct Element Modelling are generated mainly through three resources as simplification of physical model, determination of parameters and boundary conditions. A measure of errors which represent the degree of numerical solution 'close to true value' is proposed through fuzzy probability in this paper. The main objective of this paper is to estimate the reliability of Distinct Element Method in rock engineering practice by varying the parameters and boundary conditions. The accumulation laws of standard errors induced by improper determination of parameters and boundary conditions are discussed in delails. Furthermore, numerical experiments are given to illustrate the estimation of fuzzy reliability. Example shows that fuzzy reliability falls between 75%-98% when the relative standard errors of input data is under 10 %.
