• Explosives and Blasting
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• v.14 no.1
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• pp.49-63
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• 1996

Blasting is a work that destruct an object by use of explosive. Its use covers a wid range, and it is applicable to blast the rocks, minerals, coal, steel and concrete structures, bridges, etc. To execute the blast plan most effectively, the properties of the object and the explosives should be well understood, and all the other conditions must ve incorporated in its design and plan. A safe blasting pattern and procedure should be selected considering the envirinmental effects and dther conditions. At the same time, a protective protective pricedures should be utilized to prevent the safety hazards such as the excessive blast vubration, air pressure, and the flying fragments. This study reviews the controlled blasting techniques in these regards.

• Explosives and Blasting
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• v.29 no.2
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• pp.81-88
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• 2011

Most of the blast pollution that causes complaints is noise and vibration. Hence, special attentions need to be paid to controlling the underwater noise in designing blasting for those areas. This study estimated underwater sound pressure using distance from blasting and charge per delay and underwater sound pressure level using the underwater sound pressure. To identify the validity of the estimated value, the study demonstrated the results at other areas and compared actual results with estimated results.

• Explosives and Blasting
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• v.19 no.3
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• pp.67-74
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• 2001

발파진동이 건설 환경공해의 한 요인으로서 심각한 사회문제로 대두되자, 이 부분에 대하여 여러 가지 연구활동이 각 관련기관 및 단체에서 이루어져 왔다. 기존의 대부분이 안전 한계치를 제시하는 과정에서 통계처리를 이용한 회귀분석 등이 이용되었으나, 최근에 즈음하여 진동파형 및 주파수 분석 등을 통한 진동 특성을 연구하거나, 발파 진동에 의한 구조물의 응답특성에 관한 연구 등이 병행하여 수행되고 있다. 지진하중에 대한 내진설계 분야에서 구조 동역학적인 개념이 도입 된지 이미 오래지만, 발파공학의 분야에 접목시키기 위한 노력은 최근에 들어와서 보다 활발히 진행되고 있다. 이러한 시도에서 응답스펙트럼은 가장 기초적이며 필수적인 사항 중의 하나라고 생각되며, 본 고찰에서는 이러한 응답스펙트럼의 작성에 대한 기본적인 이해와 몇가지 작성 예를 들어 그 의미를 상기 해보고자 하였다.

• Explosives and Blasting
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• v.15 no.1
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• pp.26-35
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• 1997

The research on the safe and efficient demolition technique of building structure is required due to rapid increase in structures which has the problems of safety and endurance. The techniques of blasting and operation and parameters affecting the efficiency of demolition were analyzed to maximize demolition efficiency. And successful, unsuccessful and near-miss demolition cases in Korea have been investigated so that demolition operation can be safe and accurate.

• Explosives and Blasting
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• v.20 no.1
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• pp.51-59
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• 2002

터널의 1차 숏크리트 라이닝의 설계선 외측 부분에 발파시 주변에 방사상으로 발생되는 균열들을 제어하기 위하여 Smooth-Blasting 공법 적용하여 외곽공에 정밀폭약(FINEX)을 사용한다. (주)한화에서는 Smooth-Blasting 공법의 중요사항인 폭약선정 사항에서 기존의 G/D 계열의 FINEX를 동일 발파조건에서 여굴 및 미굴의 발생을 최소화시키기 위해 에멀tus 계열의 New FINEX를 개발하여 약종 별로 동일 발파 패턴을 적용하여 터널에서 시험발파를 실시하여 천단 및 좌, 우 측벽의 여굴의 발생범위를 계측장비와, Half Casting을 측정하였다. 현장 시험발파를 통한 결과 흑운모 편마암(일축강도 $1500{\;}Kg/\textrm{cm}^2)$에서 여굴제어는 G/D계열의 FINEX 보다 EMX 계열의 New FINEX가 15~50% 정도 감소가 되었다.

• Explosives and Blasting
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• v.30 no.1
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• pp.52-59
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• 2012

Fragmentation is one factor to affect productivity and economic efficiency of mine. Authors investigate the blasting pattern and improve it step by step while observing the fragmentation results. Final pattern was suggested and the fragmentation improved 24 % compared to traditional pattern, with 50 % is passing rate.

• Explosives and Blasting
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• v.19 no.1
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• pp.41-52
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• 2001

본 연구에서는 터널발파 진동의 전달 특성을 규명하기 위하여 네 곳의 도로터널의 '터널내부','터널외부','터널직상부 진행방향', '터널직상부' 진행직각방향'의 네 방향으로 발파진동을 계측하였다. 지발당장약량을 해당 지발당장약량과 최대 지발당장약량으로 구분하였으며, 지반의 진동 전달 특성을 확인하기 위하여 자승근 환산거리와 입방근 환산거리로 회귀분석 하였다. 또한 PPV, 거리별 우세진동수를 구하였다. 터널상부 진행방향의 진동 수준이 가장 크게 나타났으며, 진동의 감쇠도 크게 이루어졌다. 터널내부는 비교적 고주파성분이 우세하였으나 나머지는 일반적인 경향을 보였다 최대 지발당장약량을 적용한 입방근 환산거리의 경우가 거리별 감쇠특성 및 상관성이 다른 비교대상에 비하여 가장 우세하게 나타났다. 따라서 최대 지발당장약량을 적용한 입방근 환산거리 방식으로 설계하는 것이 바람직하다고 판단된다.

• Tunnel and Underground Space
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• v.16 no.2 s.61
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• pp.189-193
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• 2006

Guide line for rock blasting design of the MOCT(Ministry of Construction and Transportation) adopts attenuation equation of du Pont company for blasting vibration. In this paper the validity of the equation is studied by comparing with several equations of Dowding, Devine and author's induced from the domestic vibration data. The equation is inadequately used and predicted values are proved to be underestimated and causes vibration problems.

• Explosives and Blasting
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• v.39 no.3
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• pp.44-64
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• 2021

With the upcoming 4^th industrial revolution era, the applications of artificial intelligence(AI) and big data in engineering are increasing. In the field of blasting, there have been various reported cases of the application of AI. In this paper, AI techniques, such as artificial neural network, fuzzy logic, generic algorithm, swarm intelligence, and support vector machine, which are widely applied in blasting area, are introduced, The studies about the application of AI for the prediction of ground vibration, rock fragmentation, fly rock, air overpressure, and back break are surveyed and summarized. It is for providing starting points for the discussion of active application of AI on effective and safe blasting design, enhancing blasting performance, and minimizing the environmental impact due to blasting.

• Explosives and Blasting
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• v.41 no.3
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• pp.26-37
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• 2023

In order to determine the amount of explosives that can minimize the vibration generated during tunnel construction using the blasting method, it is necessary to derive the blasting vibration coefficients, K and n, by analyzing the vibration records of trial blasting in the field or under similar conditions. In this study, we aimed to develop a technique that can derive reasonable K and n when trial blasting cannot be performed. To this end, we collected full-scale trial blast data and studied how to predict the blast vibration coefficient (K, n) according to the type of explosive, center cut blasting method, rock origin and type, and rock grade using deep learning (DL). In addition, the correction value between full-scale and borehole trial blasting results was calculated to compensate for the limitations of the borehole trial blasting results and to carry out a design that aligns more closely with reality. In this study, when comparing the available explosive amount according to the borehole trial blasting result equation, the predictions from deep learning (DL) exceed 50%, and the result with the correction value is similar to other blast vibration estimation equations or about 20% more, enabling more economical design.