## 8 Conclusions  
In this study, the collapse settlement and coefficient of stress release of sandy gravel soil were examined. To develop the prediction model, a dataset consisting of 180 samples from a large-scale direct shear test was employed. Using sand content (SC), normal stress (\(\sigma_{n}\)), shear stress level (SL), and relative density (Dr) variables, the developed models could predict the collapse settlement and the coefficient of stress release. The findings of the present study can be summarized as follows:  
(1) EPR models developed with an exponential function were selected as the optimal models. According to the R\({}^{2}\) coefficient, the levels of precision of the model in predicting collapse settlement using training, testing, and all data were 0.9759, 0.9759, and 0.9759, respectively, and the precision levels in predicting the coefficient of stress release were 0.9833, 0.9820, and 0.9833, respectively;  
(2) The EPR models showed superior performance in predicting the collapse settlement and coefficient of stress release compared to the MGGP model. Additionally, the ANN model showed higher accuracy than the EPR model in predicting collapse settlement. However, the EPR model could predict the coefficient of stress release more precisely compared to the ANN model;  
(3) The results of the sensitivity analysis revealed that the SC was the most important and Dr the least important parameter in predicting the collapse settlement. Furthermore, the Dr and SC were found to be the most and least important parameters, respectively, in predicting the coefficient of stress release;  
(4) The results of the parametric study confirmed that increases in the SL and SC led to an increase in collapse settlement and a decrease in the coefficient of stress release.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} **Input** \\ **Parameters** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Input** \\ **Parameter** \\ **Changes** \\ \end{tabular} } & \multicolumn{2}{c}{**Collapse Settlement Changes**} & \multicolumn{3}{c}{**Stress Release Changes**} \\ \cline{3-8}  & & **[PERSON]** & **[PERSON]** & **This** & **[PERSON]** & **[PERSON]** & **This** \\  & & **et al. [46]** & **et al. [47]** & **Study** & **et al. [46]** & **et al. [47]** & **Study** \\ \hline \(\sigma_{n}\) & Increase & Increase & Increase & Increase & Increase & Increase & Increase \\ SC & Increase & Increase & Increase & Increase & Decrease & Decrease & Decrease & Decrease \\ SL & Increase & Increase & Decrease & Decrease & Decrease & Increase & Increase \\ Dr & Increase & Decrease & Decrease & Decrease & Decrease & Decrease & Decrease \\ \hline \hline \end{tabular}
\end{table}
Table 12: Comparing the influence of input parameter changes on collapse settlement and coefficient of stress release.  
Additionally, increasing \(\sigma_{\rm n}\) caused both collapse settlement and coefficient of stress release to increase. Finally, increases in the Dr variable reduced the collapse settlement and increased the coefficient of stress release;  
(5) To continue this research and achieve higher accuracy in predicting the collapse settlement and coefficient of stress release, other machine learning methods could be used.  
Conceptualization, A.R.G., A.D., P.F. and D.J.A., methodology, A.R.G. and A.D.; software, A.R.G. and A.D.; formal analysis, A.R.G. and A.D.; writing--original draft preparation, A.R.G., P.F., D.J.A. and A.D., writing--review and editing, A.R.G., A.D., P.F. and D.J.A., supervision, A.R.G. and D.J.A. All authors have read and agreed to the published version of the manuscript.  
This study received no external funding.  
Not applicable.  
All the data, models, and code that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.