Comparing blast-induced ground vibration models using ANN and empirical geomechanical relationships

Abstract Blasting remains as an economical and reliable excavation technique, but there are some environmental shortcomings such as the control of blast-induced vibration. The impacts of vibration over surrounding communities in a blast area have been investigated for decades and researchers have been using a myriad of empirical predictive attenuation equations. These models, however, may not have satisfactory accuracy, since parameters associated to geomechanical properties and geology affect the propagation of seismic waves, making vibration modeling a complex process. This study aims for application of an Artificial Neural Network (ANN) method and Geomechanical parameter relationships to simulate the blast-induced vibration for a Brazilian mining site and then compare them to the traditional approach. ANN had the best performance for this mine despite having demanded large datasets (as much as for the traditional approach), while geomechanical parameters like RQD and GSI may be used to deliver a fair approach even without seismic data. Also, ANN methods may be useful in dealing with a large amount of information to facilitate the simulation process when combined with other methods. Therefore, alternative prediction methods may be helpful for small budget mining operations in planning and controlling blast-induced vibration and helping mining in urban areas becoming a more sustainable activity.