Abstract Description: Dam safety is a critical priority in the mining industry, especially for tailings storage facilities, where proactive monitoring is vital for early risk detection. This paper introduces a methodology to enhance piezometer monitoring through the use of open-source programming software, integrating real-time remote sensors, 2D cross-sectional visualizations, and machine learning anomaly detection to improve tailings dam safety. We demonstrate the development of a custom script designed to ingest sensor data and manage large datasets efficiently. The integration of 2D cross-sectional visualizations provides a clearer understanding of groundwater pressure distribution and flow behavior within the dam structure. Additionally, we explore the application of machine learning models for anomaly detection, enabling the identification of emerging risks through advanced pattern recognition before they escalate into critical failures. A case study is presented, showcasing our data management framework for processing large, complex datasets in real-time and facilitating automated risk alerts. The paper highlights key trends observed in the data, emphasizing the role of advanced monitoring technologies in improving early detection and predictive risk management. Ultimately, the integration of these technologies enhances operational reliability and decision-making, providing more effective risk mitigation for tailings dams.
Learning Objectives:
Learn about emerging technologies about monitoring piezometers in dams.
Learn about data management techniques to handle large volumes of data from automated collection systems.
Learn about easy Machine Learning techniques for data trend analysis.
