A Neuro‑numeric Approach for Flyrock Prediction and Safe Distances Definition - Mining, Metallurgy & Exploration (2021)

In spite of the fact that flyrock phenomenon represents the real threat for personnel and machinery, it still remains insufficiently investigated. There are efforts, in the mining community, to explain the causes of flyrock, but the models capable to accurately predict flyrock occurrence and define the flyrock distance still do not exist. This article is an attempt to establish such a predictive model. The model utilized the adaptive nature of artificial neural networks and combined it with the accuracy of numerical modeling of physical phenomena. Necessary data were collected by high-speed camera recordings of actual blasts at three different surface mines and processed for further use for artificial network training. The result was a neuro-numerical couple which was capable to predict flyrock occurrence, estimate the launch velocity of flyrock fragments, and calculate the maximum distance of flyrock fragments. The safe distance was then calculated by multiplying the flyrock distance with a factor of safety. The article explains the procedure for data acquisition and processing, artificial network construction, training, and validation. It also explains the principles of flyrock distance calculation and, finally, provides a definition of factor of safety and safe distance. The results showed that the prediction was satisfactorily accurate and reliable.