Prediction of flyrock launch velocity using artificial neural networks

Due to a potential to cause damage to machinery and structures and cause injuries to personnel, flyrock is the most dangerous adverse effect of blasting operations. Because of that, it is of primary importance to predict flyrock events and maximum range of flyrock fragments in order to define safety limits and secure the perimeter. There are various models for flyrock range prediction, and most of them rely on proper calculations of flyrock launch velocity. However, a unique and universally applicable model of launch velocity prediction still does not exist. Work presented in this article is a concept of adaptive system application for the prediction of flyrock launch velocities. It shows the principles of input data selection, acquisition and processing and presents the principles of design, training, validation and verification of applied artificial neural network.