## 8 Conclusions  
The calibration results indicated an optimal and accurate fit of the simulated curves to the experimental curves obtained from CIU triaxial tests. It is concluded that the constitutive models addressed in this study are capable of adequately reproducing the behavior exhibited by the mining tailings soil classified as low-plasticity silt. Through the methodology implemented in this research, it was possible to identify key factors controlling the configuration of the simulated curves. Based on a sensitivity analysis, error assessment, and validation using the axisymmetric model, the best curve representation was achieved. The analysis revealed that an intermediate dataset size of 250 data points provided the most accurate representation of the stress-strain curve, effectively capturing both the smooth transitions and critical inflection points. This dataset size allowed for a precise modeling of soil behavior without overfitting the model or omitting essential information due to insufficient data. This finding is crucial for future calibration studies, as it optimizes the time and computational resources required for a reliable soil behavior representation. The automation of the HSS model represents a significant advancement in the calibration of curves using constitutive models. It also demonstrates the functionality and simplicity with which a code can be developed to enable a more dynamic calibration process. In addition, the automation of the NS model is proposed for future studies and recommended for implementation by researchers. Despite the results obtained in the calibration and simulation of soil behavior using the evaluated constitutive models, this study presents certain limitations that must be considered. Firstly, the analysis focused exclusively on the dilative behavior of a silty soil under fully saturated conditions (100%) and along undrained loading paths, which restricts the scope of the conclusions drawn. To move toward a more robust implementation, it is recommended to extend the study to drained conditions and to soils with contractive behavior, which would allow for evaluating whether the variables calibrated in this research remain optimal or require significant adjustments. Likewise, the main limitations of this research stemmed from the gap in the literature with regard to the specific topic addressed. Although the literature that exists regarding constitutive models is extensive, most of it focuses on the HSS model, which benefits from broader empirical validation and is widely adopted in the mining industry. In conclusion, the observed differences between the constitutive models stem from the theoretical foundations on which each is based. It was demonstrated that although the NS model is more suitable for evaluating phenomena such as static liquefaction--due to its emphasis on plasticity and critical state theory--the HSS model proved to be more accurate in calibrating the experimental curves. This is because, as previously mentioned, the HSS model is capable of simulating static liquefaction under undrained conditions, provided that there are no changes in the void ratio [20], thereby offering a more faithful representation of the conditions observed in the laboratory tests. Based on these results, it is concluded that the HSS model provides a more consistent representation compared to NS. However, it is important to note that for drained loading analyses, the NS model remains the most appropriate tool for assessing static liquefaction, given its formulation and empirical support. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15158726/s1](https://www.mdpi.com/article/10.3390/app15158726/s1)]([https://www.mdpi.com/article/10.3390/app15158726/s1](https://www.mdpi.com/article/10.3390/app15158726/s1)), SI: Code for the automation of the calibration of the HSS constitutive model. Conceptualization, R.G. and M.M.-G.; methodology, E.A. ; software, R.M. ; validation, P.F.P. and C.C. ; formal analysis, R.G.