• Explosives and Blasting
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• v.28 no.1
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• pp.1-10
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• 2010

In blasting for lighting, fatigue behaviors of pillars such as destruction and deformation may occur due to blasting vibration and flyrock, which may cause collapses of cavities. This study aims to identify dynamic behavior of pillars to maintain efficient safety of cavities in large drafts. when they collide with flyrocks under blasting for the excavation. For the purpose, we compared ground vibration around pillar when flyrock collided with the pillar and that when explosive blast happened for the excavation. we conducted fragmentation analysis of the flyrock and compared impact vibration obtained from empirical equation with ground vibration obtained from regression analysis of real vibration data. also we compared those with results analyzed from numerical analysis.

• Explosives and Blasting
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• v.30 no.2
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• pp.9-20
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• 2012

These days, mining industry prefers underground development for large mining because of exhaustive minning resources and large drafts and mining cavities thanks to extensive distribution of heavy excavation machines. In a mining design, to control collapse of cavities and secure stability, design of cavities and pillars are considered as very important. Therefore, this study obtained a prediction equation of blasting vibration through instrumentation for underground cavities. And we obtained theoretical shock vibration imposed on pillar through fragmentation analysis and measurement of flyrock distance. To examine the influence of pillar in underground mining blasting, we carried a finite element analysis and compared the result with prediction equation of blasting vibration, and shock vibration of flyrock when a impact was imposed on pillar and theoretical shock vibration.

• Explosives and Blasting
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• v.23 no.2
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• pp.57-66
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• 2005

A major concern with blasting at surface mines is generation of ground vibration, air blast, flyrock, dust & fume and their impact on nearby structures and environment. A study was conducted at a coal mine in India which produces 10 million tonne of coal and 27 million cubic meter of overburden per annum. Draglines and shovels with dumpers carry out the removal of overburden. Detonation of 100 tonnes of explosives in a blasting round is a common practice of the mine. These large sized blasts often led to complaints from the nearby inhabitants regarding ground vibrations and their affects on their houses. Eighteen dragline blasts were conducted and their impacts on nearby structures were investigated. Extended seismic arrays were used to identify the vibration characteristics within a few tens meters of the blasts and also as modified by the media at distances over 5 km. 10 to 12 seismographs were deployed in an array to gather the time histories of vibrations. A signature blast was conducted to know the fundamental frequency of the particular transmitting media between the blast face and the structures. The faster decay of high frequency components was observed. It was also observed that at distances of 5km, the persistence of vibrations in the structures was substantially increased by more 10 seconds. The proximity of the frequency of the ground vibration to the structure's fundamental frequencies produced the resonance in the structures. On the basis of the fundamental frequency of the structures, the delay interval was optimized, which resulted into lower amplitude and reduced persistence of vibration in the structures.

• Tunnel and Underground Space
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• v.31 no.1
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• pp.25-40
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• 2021

As the era of Industry 4.0 arrives, the researches using artificial intelligence in the field of rock engineering as well have increased. For a better understanding and availability of AI, this paper analyzed the types of algorithms and how to apply them to the research papers where AI is applied among domestic and international studies related to tunnels, blasting and mines that are major objects in which rock engineering techniques are applied. The analysis results show that the main specific fields in which AI is applied are rock mass classification and prediction of TBM advance rate as well as geological condition ahead of TBM in a tunnel field, prediction of fragmentation and flyrock in a blasting field, and the evaluation of subsidence risk in abandoned mines. Of various AI algorithms, an artificial neural network is overwhelmingly applied among investigated fields. To enhance the credibility and accuracy of a study result, an accurate and thorough understanding on AI algorithms that a researcher wants to use is essential, and it is expected that to solve various problems in the rock engineering fields which have difficulty in approaching or analyzing at present, research ideas using not only machine learning but also deep learning such as CNN or RNN will increase.