AbstractThe probability density distribution of soil parameters is of ultimate importance in reliability analysis of a geotechnical structure. Constructing the probability density function often needs a large number of experimental data, which is not easily obtained. The dependence structure between two different soil parameters may provide quantitative correlation between the parameters. This study investigated the probability density distribution and the dependence structure of several soil parameters (i.e., cohesion, friction angle, water content, compression modulus, and void ratio) using a large number of data obtained in a construction site in Shenzhen, China. The dependence structure of three random variables is investigated for the first time. An example on estimating bearing capacity and settlement of a shallow foundation is demonstrated using the constructed dependence structures for reliability analysis. The results show that the optimal probability distributions for cohesion and friction angle are normal distributions. The compression modulus and the void ratio follow lognormal distribution. The water content follows a gamma distribution. The Frank copula is the best dependence structure of cohesion and friction angle. The Clayton copula is the best dependence structure of compression modulus, void ratio, and water content. The results show that different dependence structures of soil parameters can significantly affect the probability of failure. The traditional Gaussian copula may overestimate the failure probability in terms of bearing capacity.