Al-Based Rockfall Prediction and Alert System for Open-Pit Mines

Abstract

 Rockfall hazards in open-pit mines present serious threats to worker safety, equipment, and uninterrupted mining operations. This
project proposes an AI-based rockfall prediction and alert system that provides early warningsto prevent accidents and reduce risks. The system
integrates Artificial Intelligence (AI), Machine Learning (ML), and Internet of Things (IoT) technologies to monitor and analyze environmental
and geological conditions in real time. Various sensors such as seismic sensors, tiltmeters, and weather monitoring devices are deployed to
collect critical data, including ground vibrations, slope movement, and rainfall intensity. Additionally, cameras or drones are used to capture
images for detecting cracks and structural changes in rock formations. The collected data is processed and analyzed using machine learning
algorithms trained on historical and real-time datasets to identify patterns associated with potential rockfall events. Based on this analysis, the
system predicts the likelihood of rockfall and generates alerts when risk levels exceed predefined thresholds. These alerts are communicated
through alarms, mobile notifications, and monitoring dashboards, enabling timely preventive actions and evacuation. The proposed system
enhances safety, minimizes damage, and improves operational efficiency by enabling proactive decision-making. Overall, it provides a reliable
and intelligent solution for managing rockfall risks in open-pit mining environments.