Investigation of blast-induced ground vibrations in the Tulu boron open pit mine

GÖRGÜLÜ K., ARPAZ E., DEMİRCİ A., Kocaslan A., Dilmac M. K., YÜKSEK A. G.

BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT, cilt.72, ss.555-564, 2013 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 72
  • Basım Tarihi: 2013
  • Doi Numarası: 10.1007/s10064-013-0521-4
  • Dergi Adı: BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.555-564
  • Anahtar Kelimeler: Blasting, Ground vibration, Site-specific constants, Directional changes, Rock mass properties, Artificial neural networks, SARCHESHMEH COPPER MINE, NEURAL-NETWORK, PREDICTION, FREQUENCY
  • Sivas Cumhuriyet Üniversitesi Adresli: Evet

Özet

Blasting, which is widely used in hard rock mining, construction, and quarrying, can have a considerable impact on the surrounding environment. The intensity of the blast-induced ground vibration is affected by parameters such as the physical and mechanical properties of the rock mass, characteristics of the explosive, and the blasting design. The rock characteristics can change greatly from field to field or from one part of a bench to another part, and can have directional variability according to discontinuities in the geological formation and structure. In this study, field measurements were carried out and their results were evaluated to determine blast-induced ground vibrations at the Eti Mine Tulu Boron Mining Facility, Turkey. Our results showed different field constants for the propagating blast vibrations depending on the direction of propagation (K = 211.25-3,671.13 and beta = 1.04-1.90) and the damping behavior of the particle velocity. Additionally, we found that the field constants decrease as the rock mass rating (%) values diminishes. A much higher correlation coefficient (R (2) = 0. 95) between the predicted and measured peak particle velocity (PPV) values was achieved for our modeling studies for PPV prediction using artificial neural networks compared with classical evaluation methods.