## 4 Discussion
### The Importance of Targeted Field Investigation in Practice
If the suggestions related to the horizontal distance from the wall and the domain length (\(x/H=0\) and \(d_{d}/H=1\) respectively) are valid, the \(F_{predicted}/F_{actual}\)- and \(M_{predicted}/M_{actual}\)- ratios for this specific  
Figure 21: Example #2: \(F_{predicted}/F\)-\({}_{actual}\)- and \(M_{predicted}/M\)-\({}_{actual}\)- vs. \(x/H\) curves for various \(d_{d}/H\) values and for both the sliding and overturning failure case (see also Table 1 and Figure 18). Figure 22: Example #3: \(F_{predicted}/F\)-\({}_{actual}\)- and \(M_{predicted}/M\)-\({}_{actual}\)- vs. \(x/H\) curves for various \(d_{d}/H\) values and for both the sliding and overturning failure case (see also Table 1 and Figure 19). Figure 20: Example #1: \(F_{predicted}/F\)-\({}_{actual}\)- and \(M_{predicted}/M\)-\({}_{actual}\)- vs. \(x/H\) curves for various \(d_{d}/H\) values and for both the sliding and overturning failure case (see also Table 1 and Figure 1). sampling scenario should logically be equal to unity or very close to this value. The readers should bear in their mind that a \(F_{\textit{predicted}}/F_{\textit{actual}}\)- or \(M_{\textit{predicted}}/M_{\textit{actual}}\)- value close to unity or equal to unity for a \(x/H\) value other than zero does not indicate that this \(x/H\) location is an optimal sampling location. As the soil retained by the wall is a spatially random field, a set of samples taken from points away from the wall face may also give (coincidentally) a mean value equal (or approximately equal) to the respective one obtained from a set of samples taken from the \(x/H=0\) location. As shown in Figures 20-22, the \(F_{\textit{predicted}}/F_{\textit{actual}}\)- and \(M_{\textit{predicted}}/M_{\textit{actual}}\)- ratio values for \(x/H=0\) are very close to unity or equal to unity, indicating the validity of authors' suggestions. Indicatively, it is mentioned that the abrupt drop of the \(F_{\textit{predicted}}/F_{\textit{actual}}\)- (or \(M_{\textit{predicted}}/M_{\textit{actual}}\)-) versus \(x/H\) curves in Figure 20 between \(x/H=0.5\) and \(1.5\) is attributed to the \"dark\" (strong) area appearing at this particular location, as shown in Figure 1. From Figures 20-22, it is also confirmed that a vertical sampling domain of length equal to the wall height gives better prediction for the destabilizing forces acting on the wall. A comparison between the figures given in Section 3 with the respective ones given in Section 4 shows clearly that statistical uncertainty does not necessarily decrease with the increasing number of samples. Indeed, the opposite may easily happen. For example, comparing the \(p_{f}\approx 0.03\) value for \(x/H=0\) shown in Figure 3f (single point case) with the \(p_{f}\approx 0.26\) value for \(x/H=2\) (case of 24 sampling points) shown in Figure 11f, it is obvious that statistical uncertainty can only be minimized by targeted field investigation. Such examples can also be found in the present section; please compare the case of \(\{x/H=2\), \(d_{d}/H=1\}\) with the \(\{x/H=0\), \(d_{d}/H=0.25\}\) in Figure 21b giving \(R_{d,M}\approx-0.42\) and \(-0.12\), respectively.