## 5 Calibration Automation in Python  
The calibration process automation was developed in Visual Studio Code, version 1.92 sing the Python programming language and represents a significant advancement in optimizing calibration efficiency. This automated approach adds value by simplifying and accelerating a process that is traditionally time-consuming and computationally intensive. The developed code not only facilitates the import of experimental data but also offers interactive tools to adjust parameters accurately and efficiently, reducing calibration time and improving result accuracy.  
The Python code was designed to create a web-based user interface, which connects directly to the SoilTest tool in Plaxis 2D through a custom-built private server. This enables  
Figure 16: Comparison of simulated curves from the HSS model and the axisymmetric model. (**a**) Stress–strain strength curve. (**b**) Pore pressure–strain curve. (**c**) p\({}^{\prime}\)–q stress path curve.  
Figure 17: Comparison of simulated curves from the NS model and the axisymmetric model. (**a**) Stress–strain strength curve. (**b**) Pore pressure–strain curve. (**c**) p\({}^{\prime}\)–q stress path curve.  
smooth interaction and allows comparison between experimental data and the model, eliminating the need for repetitive manual adjustments. The process begins with the option to select whether a \"Drained\" or \"Undrained\" analysis will be performed, along with the definition of the axial strain to be applied.  
In this way, the system automatically reads the data and generates the experimental curves. The interface is designed to be intuitive, featuring a preview section that automatically generates an initial parameter range based on the input data, providing a starting point for the calibration process. Figure 18 shows the flowchart of the calibration and visualization process for the HSS constitutive model using Python.  
The code includes a \"Calibration\" tab, where users can control and modify all relevant parameters. This tab allows users to make necessary adjustments to the HSS model, which are then introduced into the Plaxis environment via the SoilTest tool. Simulated curves are automatically generated and updated in real time with each parameter change, providing immediate visual feedback on how the curves evolve, greatly aiding calibration accuracy. Users can iteratively fine-tune the parameters until the simulated curves optimally match the experimental ones.  
The automation enables quick and dynamic adjustments, offering users immediate feedback on the impact of parameter changes. This significantly improves the efficiency of the process and allows for a broader exploration of parameter combinations to achieve a more precise calibration. Figure 19 shows the user interface generated by the code.  
Additionally, the procedure used to implement the developed code is described in detail, with usage notes provided in Supplementary Material S1. This practical approach enables anyone with basic knowledge of Python and the HSS constitutive model to implement and adapt the code in their soil analysis or tailings modeling projects.  
Figure 18: Flowchart illustrating the calibration and visualization process of the HSS constitutive model using Python. Developed by the authors.