## 1 Introduction
Through the methodology implemented in this study, key factors governing the configuration of the simulated curves were identified. A combination of sensitivity analysis, error analysis, and validation using an axisymmetric model allowed for an optimal representation of the experimental results. The main innovation of this study lies in the development and implementation of a semi-automated calibration methodology for constitutive models using the SoilTest module of PLAXIS 2D (version 2024.1 was used) in combination with a Python script. This tool enables continuous and efficient calibrations without the need to adjust parameters for each confinement condition manually. In this way, the time invested in the process is significantly optimized, and the selection of relevant variables for simulating soil behavior is improved, representing a methodological advancement over more traditional, manual, and segmented approaches. The contribution of this research resides not only in the efficiency of the calibration process but also in its universal applicability: the proposed methodology can be adapted to any constitutive model, regardless of its complexity or number of variables. This flexibility broadens the scope of geotechnical analysis in tailings engineering projects. In particular, it is highlighted that although the NS model presents a larger number of input parameters, its formulation based on the critical state allows for a more accurate capture of the mechanisms associated with static liquefaction in silty soils, underscoring its potential as an advanced tool for stability assessment under undrained conditions. The purpose of this research is to compare the NS and HSS constitutive models to evaluate static liquefaction in copper tailings storage facilities. A sensitivity analysis was conducted to calibrate the parameters of both constitutive models in PLAXIS 2D, aiming to identify which model more accurately captures soil behavior under static liquefaction conditions, based on CIU test results. First, the response differences between the two models are evaluated through sensitivity analysis, identifying the influence of each model's parameters on the calibration process. Second, the calibration error is compared with the amount of data used to determine which constitutive model offers a more accurate representation based on real test data [19]. Subsequently, the model comparison is validated by simulating an axisymmetric model in PLAXIS 2D, in order to establish which of the selected models exhibits a better correlation with empirical results. This study proposes a methodological framework for calibrating the experimental curves of CIU triaxial tests using PLAXIS 2D, leveraging the SoilTest module and implementing an axisymmetric model, followed by an optimization phase carried out using Python. Also, the only available data source was anisotropic undrained triaxial (CIU) tests, which limited the possibility of using traditional methods to estimate initial values, such as empirical correlations, oedometer tests, or resonant column tests. Faced with this limitation, a Python-based automatic calibration methodology was developed, which works directly with the PLAXIS 2D SoilTest tool. This strategy allowed the parameters of the constitutive models to be adjusted efficiently and consistently with the experimental curves, without relying on initial assumptions from other types of tests.