## 5 Conclusions  
In most cases, the use of sophisticated sensitivity analysis systems in geotechnical engineering is conditioned by limited data availability. One way of obtaining data is the use of numerical simulations, however, manual generation of these data requires much preparation time. In this study, a system that allows this process to be automated and results to be generated by using Monte Carlo simulations for the ranges of the geometric and soil parameter values that define a slope was developed. The system was used to build a dataset with the results of 5000 simulations, which allowed the use of regional sensitivity analysis (RSA) to study the relative influence of each of the analyzed parameters on the stability of a slope. The data were generated by using value ranges reported in the literature for residual granitic soils.  
Limit equilibrium analysis is faster than traditional finite element calculations and produces similar results in terms of safety factors, with a minimal difference between lower and upper bound values for the analyzed slopes. Furthermore, all 5000 simulations were carried out without errors, even though their parameters were randomly obtained within given value ranges, and data extraction from the models was simple by using a Jupyter notebook.  
Based on scatter plots and cumulative distribution curve graphs, it can be observed that the safety factors of the lower bound and upper bound models converge at similar values, limiting the overall safety factor range of the slope. Therefore, it can be concluded that within the slope parameter values studied in the present work, the calculated safety factors are influenced mainly by slope geometry and, to a lesser extent, by soil geomechanical parameters. The order of influence of the factors obtained in this study were slope angle > slope height > cohesion > friction angle > unit weight (\(\beta>H>c>\phi>\gamma\)).  
Conceptualization, E.M., P.L.-M., J.M.M.-C. and R.W.K.; Data curation, M.F.B.-Z.; Formal analysis, M.F.B.-Z. and E.M.; Funding acquisition, E.M.; Investigation, M.F.B.-Z., E.M. and P.L.-M.; Methodology, M.F.B.-Z., E.M. and P.L.-M.; Project administration, P.L.-M., J.M.M.-C. and R.W.K.; Resources, P.L.-M., J.M.M.-C. and R.W.K.; Software, M.F.B.-Z. and P.L.-M.; Supervision, E.M., P.L.-M., J.M.M.-C. and R.W.K.; Visualization, M.F.B.-Z., E.M. and P.L.-M.; Writing--original draft, M.F.B.-Z., E.M. and P.L.-M.; Writing--reviewand editing, E.M., P.L.-M., J.M.M.-C. and R.W.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by CIBAS 2019-01 Project.  
Not applicable.  
The data presented in this study are available upon request from the corresponding authors. The code developed to perform simulations is available in a public repository under MIT license ([[https://github.com/MBravo-Zapata/sa_maicillo](https://github.com/MBravo-Zapata/sa_maicillo)]([https://github.com/MBravo-Zapata/sa_maicillo](https://github.com/MBravo-Zapata/sa_maicillo)), accessed on 19 May 2022).  
The authors would like to acknowledge the postgraduate program of the Faculty of Engineering of the Universidad Catolica de la Santisima Concepcion during the studies of [PERSON].  
The authors declare no conflict of interest.