## 1 Introduction  
More than 98,000 reservoir dams have been built in China, with embankment dams accounting for about 93% of this total [1]. With the deepening of hydropower development in China, a large number of high rockfill dam projects have been put into construction and operation. The composition of high rockfill dam materials is complex, and their physical and mechanical parameters are greatly affected by the environment and construction, leading to significant uncertainty [2; 3; 4; 5], which makes it difficult to simulate deformation and analyze risks in dams reasonably [6; 7; 8; 9; 10]. Currently, the mechanical parameters of materials are mainly determined through laboratory tests and engineering analogy, but their accuracy is easily influenced by the number of test samples. Due to time and cost, the number of test samples in actual engineering projects is limited, making it difficult to meet the requirements of traditional statistical methods and leading to the typical problem of a small sample [11; 12; 13; 14]. To address this, many statistical methods have been developed and adopted in actual engineering projects [15; 16; 17; 18]. [PERSON] et al. [19] quantified the probability of sampling estimation bias in the population mean using the t-distribution function under the condition that the sample size of the parameter is limited. [PERSON] et al. [20] integrated prior small-sample data with field test data based on Bayesian theory, ultimately solving the posterior distribution function for the grading parameters of sand and gravel materials. [PERSON] et al. [21] proposed a Bayesian approach based on Markov chain Monte Carlo (MCMC) simulations, utilizing strength parameter test data from the Nuozhadu core wall and rockfill materials. [PERSON] and [PERSON] [22] proposed a Bayesian multi-model inference method based on multi-model inference and traditional Bayesian methods to overcome the problem of inaccurate reliability estimation of rock structures due to insufficient statistical data. Using small-sample data can not only allow the mean and standard deviation of a parameter to be determined but also support the identification of its distribution type. However, the methods mentioned above do not capture the correlation between different parameters. Moreover, many current studies focus on the spatial correlation of parameters. For instance, [PERSON] et al. [23] investigated the impact of spatial variability and the correlation of the elastic modulus, compressive strength, and tensile strength on the seismic dynamic damage of gravity dams. Their results indicated that ignoring the correlation between parameters might lead to an underestimation of the dam failure probability during an earthquake. Similarly, [PERSON] et al. [24] explored how the spatial variability of hydraulic parameters and their correlations affect the reliability evaluation of core permeability failure in rockfill dams. Correlations exist among different mechanical parameters within the same material [25]. The Duncan-Chang E-B model is widely used in the numerical simulation of rockfill dams, but obtaining its accurate parameter values in practical engineering is challenging--not only due to the limited number of test samples but also because of the correlations between parameters. Currently, there is limited research in this area. Some scholars have examined correlations between certain parameters of the Duncan-Chang E-B model and other factors like porosity and dry density; for example, [PERSON] et al. [26] established the correlation between porosity and dry density and the parameters of the Duncan-Chang E-B model for earth and rock dams through indoor experiments and statistical analysis, and then generated random fields for the mechanical parameters of earth and rock dams and conducted the stress-strain analysis of dam.