## 1 Introduction  
Soil medium displays an inherent material parameter variability. Numerical modelling of actual spatial soil variability is a considerable task, even when extensive in situ tests are carried out at the construction site. Hence, the only way to relevantly capture the uncertainty of subsoils leads to probabilistic methods. A strong recommendation for random methodology is now active, and the standards, e.g., Eurocodes, follow this. A direct introduction to the probabilistic approach in standard engineering computations is a present-day requirement and a future development perspective in the field [1, 2, 3]. Probabilistic methods are extensive due to the enormous development of computational power, introducing advanced finite element analysis (FEA) software to solve numerical problems of the subsoil media. Both the bearing capacity of foundations in their limit states (ultimate limit state--ULS) and the assessment of settlements (servicability limit state--SLS) employ probabilistic analytical means. An extensive literature review in the field is presented in, e.g., [4], including the application of structural reliability analysis (SRA) and the stochastic finite element method (SFEM) to geotechnical problems. However, the classical Monte Carlo (MC) simulation method is the most frequent, as it is easy in numerical implementation, widely available in the software market, and simple in the interpretation of its results. Many MC variance reduction techniques are developed on a high scale, e.g., stratified sampling and Latin hypercube sampling [5]. The MC simulation method is fundamental in reliability-based robust geotechnical design (RGD) [6]. The first-order reliability methods (FORM) and second-order reliability methods (SORM) are permanently developed [7]. Note that numerical results are affected by the chosen computational method and are possibly different from the in situ measurements [8; 9]. There are various ways to reflect uncertainty in soil medium parameters. The most advanced and challenging, in theoretical and numerical meaning, is the random field approach in both two-dimensional and three-dimensional versions (see, e.g., [10; 11]). A joint approach of multi-block failure mechanisms and a spatial averaging approach is shown in [12]. [PERSON] and [PERSON] [13] proposed the upper-bound and lower-bound analysis. Generation based on the Karhunen-Loeve expansion method and the classic exponential covariance function method is presented in [14]. Advanced correlation functions and the Weibull-type variables are incorporated in [15]. [PERSON] and [PERSON] [16] applied polynomial chaos expansion in bearing capacity assessment of a strip foundation; material parameter sensitivity is analysed with the use of the Sobol index here. Similar solutions are adopted in [17]. [PERSON] et al. [18] combined advanced kriging and Monte Carlo simulation (AK-MCS). [PERSON] et al. [19] combined the FEM adaptive techniques with random field application. The point estimate method (PEM) is also applied (see [20]). [PERSON] and [PERSON] [21] employed studies on the in situ results in the parameter estimation of the random field correlation function. Regardless of the adopted approaches, it is necessary to apply advanced computational algorithms, unavailable in commercial software; hence, implementation difficulties arise in real-life engineering cases. Random fields are essential in the uncertainty modelling of a subsoil based on limited in situ data [22; 23]. Attempts are made to precisely model the subsoil medium in two-dimensional [24] or three-dimensional [25] problems. Material homogenization allows for realistic subsoil modelling [26; 27]. The methods above are often applied in specific cases, e.g., column-reinforced soils [28; 29]. [PERSON] et al. [30] proposed to simulate the subsoil Young's modulus using a homogenization procedure linking the probabilistic FE analysis and the homogeneous FE analysis. This paper proposes an alternative subsoil modelling to reflect the uncertainty of material parameters.