## 7 Summary and Conclusions  
This study compared several probabilistic analysis sampling methods for predicting the final depth of burial (DoB) of munitions in sandy, clayey sand, and clayey soils. The methods included different sampling techniques within Monte Carlo simulations: (1) pseudorandom sampling, (2) Latin Hypercube Sampling, and (3) the Gaussian Process Response Surface Method. Additionally, soil uncertainties were sampled using both normal and lognormal distributions. The Poncelet model was employed as the penetration model to simulate the behavior of munitions and determine their DoB, where penetration resistance is correlated with drag coefficients, bearing resistances, and soil densities. The main findings of this study are listed below:  
1. Various sampling techniques provide comparable predictions of the DoB, suggesting that all investigated methods are effective in capturing the uncertainties associated \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Sampling Method** & **Mean DoB** & **Range of COV** & **Average COV** & **Computational Effort** \\ \hline \multirow{3}{*}{**Sand**} & PRS & 0.28 & 27–34 & 31 & 100\% \\ \cline{2-6}  & LHS & 0.28 & 27–36 & 31 & 105\% \\ \cline{2-6}  & GP\_RSM & 0.28 & 28–37 & 32 & 35\% \\ \hline \multirow{3}{*}{**Clayey Sand**} & PRS & 0.60 & 22–26 & 24 & 100\% \\ \cline{2-6}  & LHS & 0.60 & 22–27 & 24 & 102\% \\ \cline{2-6}  & GP\_RSM & 0.60 & 22–27 & 25 & 36\% \\ \hline \multirow{3}{*}{**Clay**} & PRS & 1.13 & 19–24 & 22 & 100\% \\ \cline{2-6}  & LHS & 1.12 & 19–24 & 21 & 103\% \\ \cline{2-6}  & GP\_RSM & 1.13 & 19–24 & 22 & 37\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Uncertainties in predicted DoB in sand, clayey sand, and clay. with the munitions' burial depth. It has been noticed in all simulations that PRS and LHS consumed approximately similar run time, while the GP_RSM was the fastest sampling technique. 2. Sampling uncertainties from a normal or a lognormal distribution did not significantly impact the DoB predictions. However, the high uncertainties of the studied random variables resulted in normal distribution sampling physically inadmissible values for COVs above 28%. Thus, a lognormal distribution is preferred for use in sampling Poncelet drag and bearing coefficients when conducting probabilistic predictions of the DoB of unexploded UXOs. 3. Taking into account the inherent uncertainties associated with soil properties can have important impacts on the predicted DoB. The typical coefficient of variation in the DoB is approximately 32% in sand, 25% in clayy sand, and 22% in clay. These uncertainties can significantly influence the required cleanup depth for the redevelopment of sites afflicted by the presence of UXOs, with associated financial impacts on restoring former defense and battlefield sites. 4. The uncertainty of the density, drag coefficient, and bearing coefficient primarily influenced the depth of burial (DoB) in sandy soil, whereas in clayy sand soils, the uncertainty in the bearing coefficient was the dominant factor. In clayy soil, all variables under various distribution conditions resulted in approximately identical predictions, with no single variable appearing to be dominant. The findings of this study contribute to enhancing the predictive capabilities and reliability of determining the depth of burial (DoB) for unexploded munitions (UXOs) in various soil types, crucial for the environmental cleanup of sites afflicted with UXOs. First, this work provides, for the first time, COVs for the drag and bearing parameters influencing the widely used Poncelet penetration model. In addition, the sensitivity analyses revealed the relative effects of drag and bearing coefficients on DoB predictions in different soils, which offers targeted guidance for soil characterization and optimizing data collection efforts. Conceptualization, M.O. and S.B. ; Methodology, M.I. ; Validation, M.I. ; Formal analysis, B.N.M. ; Investigation, M.O. and S.B. ; Data curation, B.N.M. ; Writing--original draft, B.N.M.