## 3 Sensitivity Analysis of the NorSand and HSSmal Models Using SoilTest  
The validation of the amount of data used to represent the deviatoric stress (q) versus mean effective stress (p) curves, through the SoilTest tool implemented in Plaxis(r), is crucial to ensure that the curve calibration is both efficient and representative. Initially, 500 data pairs were used to define the discrete points of this curve, aiming to capture the entire stress-strain behavior, including the transitions between the contractive and dilative phases. However, it was observed that a larger amount of data does not always lead to improved model accuracy. Under this scenario, what happens when the curve is over- or under-dimensioned? To address this question, a comparative analysis was conducted using datasets of 40, 250, and 1000 points. For this study, a dataset of 250 points was found to be sufficient to achieve an accurate simulation of the stress path in the p'-q diagram, as shown in Figure 4. This approach allowed for the evaluation of the model's sensitivity to the number of points used and helped determine how the curve responded under different scenarios for the three confining pressures of 400 kPa, 200 kPa, and 100 kPa. The results showed that using 250 data points provided an optimal curve configuration. Below this quantity, the curve failed to adequately capture the smoothness required to accurately represent soil behavior. Straight segments and abrupt transitions were evident, which did not align with the experimental curves. When the number of data points was undersized, the calibration did not simulate the experimental curve behavior; instead, it tended to produce a rough shape due to the lack of sufficient points for proper definition. Conversely, when the curve was oversized, it tended to exaggerate the structure of the experimental curve due to the excessive number of data points [30]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Specimen** & **Water Content [\%]** &
\begin{tabular}{c} **Effective Confining** \\ **Pressure [kPa]** \\ \end{tabular} & **B Value [\%]** & **Cell Pressure [kPa]** & **Back Pressure [kPa]** \\ \hline
1 & 16 & 100 & 98.3 & 700 & 600 \\ \hline
2 & 16 & 200 & 99.1 & 700 & 500 \\ \hline
3 & 16 & 400 & 98.8 & 700 & 300 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Initial conditions and sample preparation procedure. Figure 3: (**a**) Stress–strain curve. (**b**) Pore pressure–strain curve. (**c**) p’–q stress path curve. The sensitivity analysis performed during the calibration using the SoilTest tool was essential for understanding the impact of each parameter on the stress-strain and pore pressure curves. This analysis focused on identifying the most influential variables of the constitutive models through systematic modification of each one. Initially, a preliminary calibration was conducted to obtain an approximate fit to the experimental curves. Based on this initial calibration, a procedure was structured to individually vary each parameter [31]. To perform this analysis, a methodology was established in which each parameter governing the constitutive model was modified under three scenarios: (i) a significant decrease in the specific variable, (ii) maintaining the calibrated value from the previous step, and (iii) a significant increase in the parameter. Each variable was analyzed individually: the state parameter was varied by \(-0.03\) and \(0.03\); the hardening parameters were reduced by 90% and increased by 900%; and the critical friction ratio was varied by approximately 30%. The choice of a \(\pm 30\%\) variation range responds to the need for a sensitivity analysis that identifies which parameters govern the material behavior under dilative conditions. This approach aims to observe the overall model response to significant parameter changes and thus recognize their influence on the shape of the stress paths obtained from CIU triaxial tests.