A PORTABLE DEEP LEARNING-BASED SOLUTION FOR ROOF FALL HAZARD DETECTION

Current machine learning models used for roof fall hazard prediction are mounted on expensive sensors, computationally expensive, or lack the robustness for accurate prediction in the underground mining environment. This research aims to provide a design methodology for a robust, low-cost, deep learning-based algorithm for underground mine roof fall hazard prediction. A data sampling plan is developed to ensure the replicability and robustness of the developed model. In addition, feature engineering and transformation methods are described to identify relevant features for hazard identification. The methodology described here will be used in model development, tested, and implemented as a real-time mobile device application. The new tool will be expected to detect roof fall hazards in real-time and contribute to a crowdsourcing approach for underground hazard detection.