Abstract:
Ground vibration and air-blast over pressure are two significant undesirables,
among many environmental risks, in open-pit mining . Gaining control over the
ground vibrations generated by rock blasts had been difficult mainly due to the
complexities involved with local geology and properties of the blast. Accordingly,
existing empirical equations are only capable of making vague approximations on
the vibration frequencies based on site-specific parameters and attenuation factor.
Therefore, the available models cannot be generalized to different geo-mining
environments to obtain sufficiently reliable forecasts for ground vibration and airblast
overpressure. Hence, this study attempts to employ an Artificial Neural
Network (ANN) based feed-forward back-propagation algorithm to train a model,
using a supervised learning technique to forecast possible ground v i b r a t i on
frequencies. The main in-put parameters included in the model are noise level,
number of boreholes per single blast, depth and diameter of a borehole, charge per
hole, number of delays of the Electric Detonators (ED) in a single blast, burden and
spacing. Airblast overpressure and the ground vibration levels will be the output
by ANN model. The model was validated using 50 datasets, which were obtained
from a quarry site. After adequate training, the model can determine Peak Particle
Velocity (PPV) and frequency of Ground Vibrations (GV) for new input parameters
with a statistically significant confidence level.
Citation:
Dassanayake, S.M., Dushyantha, N.P., & Jayawardena, C.L. (2018). Applicability of a neural network model for forecasting ground vibrations in opencast mining. In A.M.K.B. Abeysinghe & G.V.I. Samaradivakara (Eds.), Proceedings of International Symposium on Earth Resources Management & Environment 2018 (pp. 29-35). Department of Earth Resources Engineering, University of Moratuwa.