Abstract: This paper proposes a Bayesian Copula identification method for shear strength parameters of soils and rocks. First, the characterization of dependence structure between shear strength parameters using Copulas is presented. Two commonly-used methods, namely least square method of Euclidean distance and akaike information criterion(AIC), for identifying the best-fit Copula, are given. Then, Monte Carlo simulations are conducted to validate the Bayesian Copula identification method. Moreover, the identification accuracy in the three methods is compared, and the main factors affecting the accuracy in the Bayesian Copula identification are identified. Finally, a total of twenty-three sets of shear strength data are compiled to demonstrate the application of Bayesian theory Copula model identification. The results indicate that with limited project-specific data and prior information, the Bayesian Copula identification method can successfully identify the best-fit Copula from a set of alternative Copulas for shear strength parameters. In comparison with the least square method of Euclidean distance and AIC, the Bayesian Copula identification method produces more accurate results for identifying the best-fit Copula. The sample size, correlation, the type of the true Copula and prior information of shear strength parameters has a significant impact on the accuracy of the Bayesian Copula selection method. Furthermore, the commonly adopted Gaussian copula for characterizing the dependence structure between shear strength parameters does not always provide the best fit to the shear strength data.

Key words: shear strength parameters, correlation, Copula function, Bayesian theory, model identification