Please use this identifier to cite or link to this item: https://doi.org/10.1007/s11431-012-4937-z
Title: Uncertainty analysis of correlated non-normal geotechnical parameters using Gaussian copula
Authors: Li, D.
Tang, X.
Zhou, C.
Phoon, K.-K. 
Keywords: Gaussian copula
geotechnical parameters
joint probability distribution function
Kendall correlation coefficient
load-displacement curve
Pearson correlation coefficient
uncertainty analysis
Issue Date: Nov-2012
Source: Li, D., Tang, X., Zhou, C., Phoon, K.-K. (2012-11). Uncertainty analysis of correlated non-normal geotechnical parameters using Gaussian copula. Science China Technological Sciences 55 (11) : 3081-3089. ScholarBank@NUS Repository. https://doi.org/10.1007/s11431-012-4937-z
Abstract: Determining the joint probability distribution of correlated non-normal geotechnical parameters based on incomplete statistical data is a challenging problem. This paper proposes a Gaussian copula-based method for modelling the joint probability distribution of bivariate uncertain data. First, the concepts of Pearson and Kendall correlation coefficients are presented, and the copula theory is briefly introduced. Thereafter, a Pearson method and a Kendall method are developed to determine the copula parameter underlying Gaussian copula. Second, these two methods are compared in computational efficiency, applicability, and capability of fitting data. Finally, four load-test datasets of load-displacement curves of piles are used to illustrate the proposed method. The results indicate that the proposed Gaussian copula-based method can not only characterize the correlation between geotechnical parameters, but also construct the joint probability distribution function of correlated non-normal geotechnical parameters in a more general way. It can serve as a general tool to construct the joint probability distribution of correlated geotechnical parameters based on incomplete data. The Gaussian copula using the Kendall method is superior to that using the Pearson method, which should be recommended for modelling and simulating the joint probability distribution of correlated geotechnical parameters. There exists a strong negative correlation between the two parameters underlying load-displacement curves. Neglecting such correlation will not capture the scatter in the measured load-displacement curves. These results substantially extend the application of the copula theory to multivariate simulation in geotechnical engineering. © 2012 Science China Press and Springer-Verlag Berlin Heidelberg.
Source Title: Science China Technological Sciences
URI: http://scholarbank.nus.edu.sg/handle/10635/59247
ISSN: 16747321
DOI: 10.1007/s11431-012-4937-z
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