Speaker
Description
The stability of tailings storage structures represents one of the major challenges in contemporary geotechnical engineering, particularly considering the environmental, social, and economic impacts associated with geotechnical failures. The increasing adoption of filtered tailings systems and dry stacking methods has expanded the use of tailings piles as an alternative to conventional disposal techniques. Despite the advantages associated with these structures, factors related to hydraulic behavior, infiltration processes, moisture variations, slope stability, and environmental conditions make their analysis a complex problem.
At the same time, advances associated with Industry 4.0 have enabled the application of Artificial Intelligence, Machine Learning, remote sensing, and real-time geotechnical monitoring techniques for identifying patterns related to structural stability. However, most existing studies remain focused on tailings dams, while only a limited number of investigations have specifically addressed tailings piles through the integrated use of multiple data sources.
In this context, the present study proposes an integrated approach based on Machine Learning techniques to identify instability precursor patterns in tailings piles using geotechnical, climatic, instrumental, and geospatial data. Models such as Random Forest, XGBoost, and Long Short-Term Memory (LSTM) networks will be evaluated to identify complex and temporal relationships within the datasets. The expected results aim to contribute to the development of methodologies for preventive monitoring, risk mitigation, and the advancement of scientific knowledge regarding the application of Artificial Intelligence in the mining ind