The mining industry plays a crucial role in the global economic development, highlighting the importance of a continuous evaluation in the physical stability of tailings deposits. All the analysis methods have limitations, and the geotechnical field monitoring is essential to understand the behavior of the geomaterials as the projects progress.
There is a need to make use of the in-situ measurements, improving the numerical predictions associated with the physical stability of the deposits. In this context, the methodology proposed by Corral (2013) is presented as an effective solution for the soil parameter update. Using sensitivity analysis, the maximum likelihood approach, and the genetic algorithm as an optimization method to solve the inverse problem.
This methodology is applied by integrating MATLAB and PLAXIS through Python. Its implementation allows reducing the uncertainty in the numerical model and obtaining more accurate predictions. Likewise, it is possible to predict with greater certainty and safety the future behavior of the following construction stages of the deposit, providing extremely valuable information for decision making in the projects.
