• Explosives and Blasting
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• v.21 no.1
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• pp.41-47
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• 2003

A large scale blasting is necessary for the construction or road, harbor or ground foundation of building and it is common that the blasting work is performed by a specialist subcontracted from the construction company who is originally responsible for the project. Sometimes the latter do not agree with the former in total amount of blasted rock. They try to find out real work amount as precisely as possible. The estimation is sometimes carried out by an entrusted person when it is impossible to come to an agreement with each other. There are several methods in estimating the blasted rock volume; a calculation by prescribed equivalents of explosive before construction, a calculation by specific charge per unit volume of rock, and a calculation by rock volume per detonator. In this study, the last method is reviewed and recommended as most reliable one.

• Tunnel and Underground Space
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• v.11 no.3
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• pp.248-256
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• 2001

The cylindrical cut is most frequently used in tunnel blast regardless of their dimensions. In this study the new parallel cut is proposed to raise advance per round, which is considered to be an elevation of the traditional cylinder cuts. The general geometric pattern of a new cut with parallel blast holes is proposed. The detailed burden and spacing between the central blasthole and those of the four section are also given. The blast results between new cut and traditional cylinder cut are given. The main results of this study are as follows. 1) The average advances per rounds in new cuts can reach 99.5% of drilling length. That of traditional cylinder cuts are known approximately 90∼95% 2) Specific charge weight of new cut compare to that of cylinder cut is approximately reduced 5% from 1.363 to 1.297 kg/㎥ 3) Specific drilling rate is also reduced 8% from 2.393 to 2.130 m/㎥ 4) Vibrations, fly rock, and fragmentation produced by the new blast are to be proved superior to those of the traditional cylinder cuts.

• Explosives and Blasting
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• v.24 no.2
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• pp.1-8
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• 2006

In this study, a charging density has been differently applied to all holes to improve an excavated length per round on excavating a tunnel in quartzite mine and to prevent a dead pressure phenomena and sintering phenomena. A composition initiating system using simultaneously a direct initiating system and a indirect initiating system with 2 detonators in one hole has been introduced to cut holes. As a bottom part which is difficult to make a free face are charged with a higher charging density and a column part are charged with a lower charging density, the composition charging and initiating system which the power of explosives works effectively in the rock mass is developed. As the results, a dead pressure phenomena and a sintering phenomena being often produced in a hard rock or in a long hole could be prevented. Besides, the workability was improved by about 15% and the specific charge was reduced to about 20%, as an excavated length vs. a drilled length per round could be increased over 95%.

• Tunnel and Underground Space
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• v.31 no.6
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• pp.508-519
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• 2021

In this study, a model was developed to predict the peak particle velocity (PPV) that affects people and the surrounding environment during blasting. Four machine learning models using the k-nearest neighbors (kNN), classification and regression tree (CART), support vector regression (SVR), and particle swarm optimization (PSO)-SVR algorithms were developed and compared with each other to predict the PPV. Mt. Yogmang located in Changwon-si, Gyeongsangnam-do was selected as a study area, and 1048 blasting data were acquired to train the machine learning models. The blasting data consisted of hole length, burden, spacing, maximum charge per delay, powder factor, number of holes, ratio of emulsion, monitoring distance and PPV. To evaluate the performance of the trained models, the mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) were used. The PSO-SVR model showed superior performance with MAE, MSE and RMSE of 0.0348, 0.0021 and 0.0458, respectively. Finally, a method was proposed to predict the degree of influence on the surrounding environment using the developed machine learning models.

• Explosives and Blasting
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• v.29 no.2
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• pp.89-99
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• 2011

Recently on Jeju island there has been a lot of development and construction. However random distribution of porous basalt and clinker seam generated from volcanic activities often interrupt and greatly reduce efficiency of blasting necessary for construction. Three test blasts were operated to solve the inefficiency problem and results indicated that a powder factor of 0.40~0.45 $kg/cm^3$ is necessary to increase the efficiency of blasting. Also the blasting scheme should be concerned whether clinker seams exists in excavation levels or not.

• Explosives and Blasting
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• v.18 no.3
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• pp.15-28
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• 2000

국내에서는 Heavy ANFO로 더 잘 알려져 있는 Emulsion Blends는 왁스 대신 오일을 사용 하여 상온에서 펌핑이 가능하도록 한 에멀젼과 ANFO(또는 초안)의 혼합물을 일컫는다. ANFO는 저렴하고 안전하며 장약이 쉽고 밀장전되는 장점이 있지만, 내수성이 거의 없고 폭발 속도가 느리며 장약 비중이 0.75∼0.90g/cc 정도로 낮아 폭약으로서 그 위력이 작은 단점을 갖고 있다. Blends는 수용성 ANFO 입자 사이의 빈 공간을 내수성 에멀젼이 태우고 있는 형태로서 에멀젼 함량 25%부터 내수성이 나타나기 시작하여 에멀젼 함량 40% 이상에서는 완전한 내수성을 갖게 되며, 에멀젼의 함량이 증가할수록 폭발속도는 카트리지 에멀젼 폭약에 근접하게 된다. 장약 비중은 에멀젼의 함량이 증가하여 45% 근처에서 1.25∼ 1.30g/cc의 최대 값을 갖지만, 그 이상의 에멀젼 함량에서는 기폭 감도 저하로 예감제를 사용하여 비중을 감소시키는 것이 바람직하다. Blends는 자체에 물을 함유하고 있으므로 열역학적으로 계산된 단위 중량당 반응열은 ANFO에 비해 매우 적지만, 폭발속도, detonation pressure(폭굉압), borehole pressure(폭발압력) 등이 ANFO에 비해 크므로 폭발압력에서부터 암석의 파괴가 가능한 압력가지의 단위 중량당 유효한 에너지의 양은 암석의 강도가 커질수록 ANFO에 비해 매우 적지만, 폭발속도, ANFO와 비슷해진다. 따라서 장약 비중이 ANFO의 130∼145%로 높은 Blends는 동일한 천공에 더 많이 장약할 수 있어 단위 천공당 암석 파괴에 이용되는 유효 에너지의 총 양이 커지게 되므로, 공간격과 저항선을 늘릴 수 있어 총 천공수를 감소시킬 수 있다. 결론적으로, Blends의 장점은 내수성과 함께 비장약량은 비슷하거나 약간 증가하는데 비해, 천공수는 크게 감소하여 전체적으로는 발파 현장의 경제성이 향상된다는데 있다.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.5
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• pp.431-449
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• 2022

As many tunnels generally have been constructed, various experiences and techniques have been accumulated for tunnel design as well as tunnel construction. Hence, there are not a few cases that, for some usual tunnel design works, it is sufficient to perform the design by only modifying or supplementing previous similar design cases unless a tunnel has a unique structure or in geological conditions. In particular, for a tunnel blast design, it is reasonable to refer to previous similar design cases because the blast design in the stage of design is a preliminary design, considering that it is general to perform additional blast design through test blasts prior to the start of tunnel excavation. Meanwhile, entering the industry 4.0 era, artificial intelligence (AI) of which availability is surging across whole industry sector is broadly utilized to tunnel and blasting. For a drill and blast tunnel, AI is mainly applied for the estimation of blast vibration and rock mass classification, etc. however, there are few cases where it is applied to blast pattern design. Thus, this study attempts to automate tunnel blast design by means of machine learning, a branch of artificial intelligence. For this, the data related to a blast design was collected from 25 tunnel design reports for learning as well as 2 additional reports for the test, and from which 4 design parameters, i.e., rock mass class, road type and cross sectional area of upper section as well as bench section as input data as well as16 design elements, i.e., blast cut type, specific charge, the number of drill holes, and spacing and burden for each blast hole group, etc. as output. Based on this design data, three machine learning models, i.e., XGBoost, ANN, SVM, were tested and XGBoost was chosen as the best model and the results show a generally similar trend to an actual design when assumed design parameters were input. It is not enough yet to perform the whole blast design using the results from this study, however, it is planned that additional studies will be carried out to make it possible to put it to practical use after collecting more sufficient blast design data and supplementing detailed machine learning processes.