## 7 Discussion of the Results  
The use of computational simulation tools, combined with the increasing availability of experimental data, has streamlined the calibration process of constitutive models, enabling more precise automation [21]. Through automation, it is possible to accelerate parameter fitting, reduce the need for manual intervention, and obtain real-time feedback regarding the accuracy of the fit relative to experimental data. This approach not only enhances analysis efficiency but also enables broader parameter exploration, ultimately contributing to the precision of the simulated models. The technical comparison between NS and HSS confirms that, although both models adequately represent silt behavior under undrained conditions, the HSS model presents computational efficiency and ease of automation compared to the NS model. Table 5 shows the summary as a comparison of the main strengths and weaknesses observed in the NS and HSS models during this study. The main strengths and weaknesses observed in the NS and HSS models during this study are comparatively summarized in Table 5, which provides a clear view of the key differences between both two constitutive approaches. Although the systematic literature review identified relevant studies related to the use of constitutive models in static liquefaction assessment, most did not address simulations applied to Mine Tailings or focus on detailed comparisons between NS and HSS models under the same conditions. For this reason, only a subset of the identified articles was useful for comparing the results of this work. Furthermore, the differences observed in the simulations, particularly in the liquefaction trigger load, are in line with those reported in previous studies, which highlight the sensitivity of the NS model to variations in density and stress state, while the HSS model tends to overestimate stiffness and underestimate post-peak strength loss in saturated soils with low plasticity. The NS model, based on CSSM, has been widely employed to simulate static liquefaction in mining tailings due to its ability to capture the sudden loss of shear strength associated with pore pressure generation under monotonic loading. NS incorporates the state parameter (\(\psi\)), enabling a realistic representation of contractive behavior and liquefaction-induced instability in loose materials such as tailings [46; 47; 48; 49]. Studies have shown that NS reliably reproduces the stress-strain response and liquefaction onset; however, it is highly sensitive to parameter calibration, requiring robust laboratory practices to ensure accuracy [47; 50; 51]. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Criterion** & **NorSand** & **Hardening Soil Small** \\ \hline Fitting experimental curves & Well, especially in the dilative and peak deviatoric stress behavior. & Accurate in small deformations, less representative post-peak. \\ \hline Static liquefaction capture & Suitable: reproduces progressive loss of resistance. & Limited: Does not represent post-liquefaction strain-softening. \\ \hline Reproduction of pore pressures & Represents accumulation in the undrained charge. & Partial: simulates initial increase but without cumulative effects. \\ \hline Sensitivity to parameters & High: requires careful calibration of initial state and density. & Moderate: sensitive to stiffness modulus and OCR. \\ \hline Practical applicability & High in critical conditions, but require greater computational effort. & High in conventional modeling; more automatable. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summarizes comparatively the main strengths and weaknesses observed in the NS and HSS models during this study. In contrast, the HSS model is an extension of the conventional Hardening Soil model, including small-strain stiffness behavior, yet it is not specifically formulated to simulate static liquefaction.