SMB: Web-Based Dam Monitoring and Data Analysis System

The monitoring and supervision of complex infrastructures, such as the dams and dikes of the Belo Monte Hydroelectric Plant, demand continuous data collection and analysis from various instruments (e.g., piezometers, surface markers, flow meters). In the context of structural health monitoring, recent advances propose the use of machine learning to recognize patterns and detect anomalies in monitoring data. This study presents a web-based application designed to support the early detection of structural anomalies through machine learning algorithms for anomaly detection, coupled with an interactive dashboard for data visualization. The system enables the establishment of baseline structural behavior and the identification of deviations that may indicate potential risks. Models were trained and tested with historical data, and multiple visualization tools were developed to facilitate the interpretation of results by engineers and decision-makers. The outcomes demonstrate that the proposed solution introduces innovative and effective technologies for the structural monitoring domain, contributing to the modernization of diagnostic processes and enhancing safety in critical energy infrastructure.