## 1 Introduction  
Uncertainties in soil properties arise from three main sources, namely, inherent variability, statistical uncertainty, and systematic uncertainties [1]. The inherent variability results from the fact that even in seemingly homogenous soil media, the soil properties exhibit variability by nature. Discrepancies between the laboratory and in situ conditions due to factors such as scale, anisotropy, and degree of saturation of soil are related to systematic uncertainties [1; 2; 3]. Statistical uncertainty (related to the mean and standard deviation of soil properties) is attributed to limited soil testing. Apparently, statistical uncertainty can be reduced by proper field investigation. Regarding the latter, it is mentioned that the current design codes are limited to some general recommendations (see Appendix A), focusing mainly on the extent of the subsurface exploration and aiming at identifying possible unfavorable geological conditions. In this respect, AASHTO [4] additionally recommends that samples be taken from locations alternating from in front of the wall to behind the wall. Apparently, this recommendation refers to structures such as sheet walls and pile walls, where undisturbed soil is retained and both active and passive states exist simultaneously. No recommendation is given for the sampling depth or distance from the wall. The effect of soil sampling on the performance of geotechnical structures has been studied only by a few researchers. In this respect, [PERSON] et al. [PERSON] et al. (2016) studied the effect of sampling on the reliability of passive earth pressure by using the random finite element method (RFEM). Considering a limited number of sampling locations (four in number), they concluded that a single sampling point located at horizontal distance equal to approximately one wall height from the wall results in a lower probability of failure independent of the spatial correlation length, and that the inclusion of additional sample points to characterize the soil properties reduces the probability of failure. [PERSON] et al. [PERSON] et al. (2016) investigated the effect of soil variability and site investigation scope on the footing's settlement of a three-story building and observed that the likelihood of under-designing or over-designing a footing decreases as the scope of the investigation increases. [PERSON] et al. [PERSON] et al. (2017) used conditional random fields enabling the site investigation data to be incorporated directly in probabilistic analysis, and they found that the coefficient of variation of factor of safety can be reduced by incorporating more site investigation data. [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) addressed the statistical uncertainties associated with the estimation of a depth-dependent trend function and spatial variation about the trend function using limited site-specific geotechnical data. This study proposed a two-step approach to characterize the uncertainties in all parameters, including the functional form of the trend, within a consistent Bayesian framework. [PERSON] et al. [PERSON] et al. (2019) studied the importance of sampling location on slope stability assessment based on statistical hypothesis testing, concluding that the slope crest is the optimal location to conduct geotechnical site exploration. [PERSON] et al. [PERSON] et al. (2019) studied the effect of the number of samples and type of trend removal on residual uncertainty. They found that removing the sample mean outperforms removing the best linear unbiased estimate (BLUE) when the actual field correlation length is small, but the BLUE is better to use if the actual correlation length is large relative to the domain size. In addition, they found that more samples reduce the uncertainty when the field correlation length is small but do not have much impact when the field correlation length is large. [PERSON] et al. [PERSON] et al.