The Study of Influence of Quarry Bench Elevation on the Prediction of Blasting Vibration Using Empirical Attenuation Equations and Artificial Neural Networks

Blasting operations in quarries are frequently carried out across benches with pronounced elevation variations, which affect the propagation of ground vibrations. This study examines vibration attenuation in a marble quarry in eastern Taiwan using both traditional empirical formulas and artificial neural networks (ANNs). Field measurements were collected from 54 production blasts, resulting in 322 vibration records at three distinct elevation levels. Several empirical equations—including an elevation correction factor—were applied and compared. Among these, the equation incorporating an adjusted elevation factor yielded higher R2 values than the other empirical models. In parallel, a three-layer ANN trained in MATLAB, using inputs such as instantaneous charge, distance, elevation difference, and total charge per blast, achieved an R2 of 0.951, highlighting total charge as a key parameter. Both the empirical and ANN methods proved effective for PPV prediction, but the ANN models demonstrated better accuracy when total charge was included.