Predicting blasting propagation velocity and vibration frequency using artificial neural networks

• Published in: International journal of rock mechanics and mining sciences (Oxford, England : 1997) Vol. 55; pp. 108 - 116
• Main Authors: Álvarez-Vigil, A.E., González-Nicieza, C., López Gayarre, F., Álvarez-Fernández, M.I.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.10.2012; Elsevier
• Subjects: Applied sciences; Artificial neural networks; Blasting; Blasting (explosive); Buildings. Public works; Computation methods. Tables. Charts; Discontinuity; Exact sciences and technology; Explosions; Geotechnics; Grounds; Miscellaneous; Particle propagation velocity; Rock; Structural analysis. Stresses; Vibration; Vibration frequency; Europe; Spain; Particle propagation velocity; Vibration; Blasting; Vibration frequency; Artificial neural networks; Propagation; Explosions; Neural network; Forecast model; Modeling; Learning; Mining; Particle; Wave propagation; Open pit mine; Blast wave; Frequency
• ISSN: 1365-1609; 1873-4545
• DOI: 10.1016/j.ijrmms.2012.05.002

We describe artificial neural networks used to predict the velocity and frequency of ground vibrations caused by blasting in an open-pit mine. The aim was to predict peak particle velocity and frequency of ground vibrations from information on the physical and mechanical properties of the rock mass, the characteristics of the explosive and blasting design. Some the parameters that could possibly have a bearing on the prediction were considered. A distinction was drawn between two kinds of parameters: those defining the surroundings in which the wave is propagated (rock type, rock mass, distance to be covered by the wave and significant subsoil discontinuities) and those defining the energy of the wave (the kind of explosive, explosive charge and blasting geometry and sequence). Vibrations were monitored using seismographs capable of capturing vibration data and transforming them into acceleration and frequency terms. To validate this methodology, the predictions obtained were compared with those obtained using conventional statistical methods. The correlation coefficients obtained for our methodology was 0.98 for peak particle velocity and 0.95 for frequency, compared to 0.50 and 0.15, respectively, for Multiple Linear Regression.