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

被引:55
作者
Gorgulu, Kazim [1 ]
Arpaz, Ercan [2 ]
Demirci, Ahmet [1 ]
Kocaslan, Arzu [1 ]
Dilmac, M. Kursat [3 ]
Yuksek, A. Gurkan [4 ]
机构
[1] Cumhuriyet Univ, Min Engn Dept, Sivas, Turkey
[2] Kocaeli Univ, Kocaeli Vocat Sch, Kocaeli, Turkey
[3] Ataturk Univ, Min Engn Dept, Erzurum, Turkey
[4] Cumhuriyet Univ, Dept Comp Engn, Sivas, Turkey
关键词
Blasting; Ground vibration; Site-specific constants; Directional changes; Rock mass properties; Artificial neural networks; SARCHESHMEH COPPER MINE; NEURAL-NETWORK; PREDICTION; FREQUENCY;
D O I
10.1007/s10064-013-0521-4
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页码:555 / 564
页数:10
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