• Geomechanics and Engineering
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• 제9권3호
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• pp.329-344
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• 2015

Cratering tests in rock are generally carried out to identify its fragmentation characteristics. The test results can be used to estimate the minimum amount of explosives required for the target volume of rock fragmentation. However, it is not easy to perform this type of test due to its high cost and difficulty in securing the test site with the same ground conditions as the site where blasting is to be performed. Consequently, this study investigates the characteristics of rock fragmentation by using the hydrocode in the platform of AUTODYN. The effectiveness of the numerical models adopted are validated against several cratering test results available in the literature, and the effects of rock mass classification and ground formation on crater size are examined. The numerical analysis shows that the dimension of a crater is increased with a decrease in rock quality, and the formation of a crater is highly dependent on a rock of lowest quality in the case of mixed ground. It is expected that the results of the present study can also be applied to the estimation of the level and extent of the damage induced by blasting in concrete structures.

• 화약ㆍ발파
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• 제36권4호
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• pp.9-15
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• 2018

발파에 의한 암석의 파쇄도는 적재, 운반과 2차 파쇄로 이어지는 후속공정에 직접적인 영향을 미치므로 파쇄도의 조절은 발파 효율성과 생산비용을 평가하는데 필수적이다. 본 연구에서는 인공절리의 방향에 따른 암석의 파쇄도에 대해 분석하였다. 갱내 암반에 수직 및 수평인공절리를 생성한 후 발파실험을 수행하였다. 생성된 암석파쇄물의 입도분포는 2차원 영상해석 프로그램인 split-desktop으로 평가하였다. 평가결과 수평인공절리가 수직인공절리에 비해 전체적인 암석파쇄물의 크기가 작게 평가되었고, 다양한 크기로 암석파쇄물이 분포하였다. 인공절리의 방향에 따라 대괴발생을 억제하고 암석파쇄물을 일정한 크기 이하로 조절 할 수 있을 것으로 판단된다.

• 터널과지하공간
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• 제23권1호
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• pp.42-53
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• 2013

천공 작업 시 드릴비트 버튼의 타격 속도와 타격 간격은 천공효율을 높이는데 있어 매우 중요한 요소이다. 따라서 본 연구에서는 버튼의 타격 속도 및 간격에 따른 암반 파쇄성능을 분석하기 위하여 홉킨스바 시험기를 이용한 타격시험을 수행하였다. 먼저, 버튼의 타격속도에 따른 암석파쇄 현상을 분석하기 위하여 단일타격 시험을 수행하였고, 수치해석을 통해 단일 타격 시험에 대한 암석의 파쇄과정을 모사하였다. 다음으로 버튼의 타격 간격에 따른 천공효율을 예측하기 위하여 타격 후 설정된 거리만큼 암석 시료를 이동시키고 재차 타격하는 방식으로 다중타격 시험을 수행하였다. 타격시험 후 암석의 천공부피는 레이저 스캐너를 이용하여 측정하였으며, 타격에너지와 천공부피를 통해 천공성능을 계산하였다. 이러한 시험 결과를 바탕으로 직경 102 mm 드릴비트의 1회 타격 시 천공성능을 예측하였다.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2001년도 봄 학술발표회 논문집
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• pp.129-136
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• 2001

Blasting method is used most engineering works for rock excavation. Blasting method is done much to upgrade of operation efficiency, contraction of construction period than other method. But blasting method happens damage by blasting vibration, nose and scattering. Therefore this study examined about effect, characteristic and application of Plasma method. To confirm effect measured vibration, noise and frequency, and analyzed data compare with general blasting.

• Structural Engineering and Mechanics
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• 제69권5호
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• pp.537-545
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• 2019

Stress analysis of bottom-hole rock has to be considered with much care to further understand rock fragmentation mechanism and high penetration rate. This original study establishes a fully coupled simulation model and explores the effects of overburden pressure, horizontal in-situ stresses, drilling mud pressure, pore pressure and temperature on the stress distribution in bottom-hole rock. The research finds that in air drilling, as the well depth increases, the more easily the bottom-hole rock is to be broken. Moreover, the mud pressure has a great effect on the bottom-hole rock. The bigger the mud pressure is, the more difficult to break the bottom-hole rock is. Furthermore, the maximum principal stress of the bottom-hole increases as the mud pressure, well depth and temperature difference increase. The bottom-hole rock can be divided into three main regions according to the stress state, namely a) three directions tensile area, b) two directions compression areas and c) three directions compression area, which are classified as a) easy, b) normal and c) hard, respectively, for the corresponding fragmentation degree of difficulty. The main contribution of this paper is that it presents for the first time a thorough study of the effect of related factors, including stress distribution and temperature, on the bottom-hole rock fracture rather than the well wall, using a thermo-poroelastoplasticity model.

• 지반과기술
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• 제3권3호
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• pp.15-22
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• 2006

The optimum blasting pattern to excavate a quarry efficiently and economically can be determined based on the minimum production cost, which is generally estimated according to rock fragmentation. Therefore, it is a critical problem to predict fragment size distribution of blasted rocks over an entire quarry. By comparing various prediction models, it can be ascertained that the result obtained from Kuz-Ram model relatively coincides with that of field measurements. Kuz-Ram model uses the concept of rock factor to signify conditions of rock mass such as block size, rock jointing, strength and others. For the evaluation of total production cost, it is imperative to estimate 3-D spatial distribution of rock factor for the entire quarry. In this study, a sequential indicator simulation technique is adopted for estimation of spatial distribution of rock factor due to its higher reproducibility of spatial variability and distribution models than Kriging methods. Further, this can reduce the uncertainty of predictor using distribution information of sample data. The entire quarry is classified into three types of rock mass and optimum blasting pattern is proposed for each type based on 3-D spatial distribution of rock factor. In addition, plane maps of rock factor distribution for each ground level are provided to estimate production costs for each process and to make a plan for an optimum blasting pattern.

• 한국터널지하공간학회 논문집
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• 제3권1호
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• pp.3-9
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• 2001

발파한 후 파쇄된 암석의 파쇄도는 발파효율을 나타내는 척도의 하나로서 발파방법을 평가하는 주요 인자이다. 파쇄도는 적재작업과 재활용을 위한 분쇄작업에 큰 영향을 미친다. 그러나 현장규모로 쌓여 있는 발파암 더미로부터 파쇄도를 조사한다는 것은 용이한 작업이 아니다. 본 논문에서는 현장 사례연구로서 터널발파에서 가장 중요한 요소인 심빼기방법 중 경사공을 이용한 V형 심빼기와 수평공 무장약공을 이용한 burn 심빼기 발파방법 중 파쇄도 측면에서 더 효율적인 방법을 선택하기 위하여 발파후 파쇄된 암을 사진촬영하여 이미지 분석을 실시하고 몇가지 파쇄입도 예측식을 이용한 분석 결과와 비교하였다.

• Geomechanics and Engineering
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• 재33권6호
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• pp.567-581
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• 2023

The prediction and achievement of a proper rock fragmentation size is the main challenge of blasting operations in surface mines. This is because an optimum size distribution can optimize the overall mine/plant economics. To this end, this study attempts to develop four improved artificial intelligence models to predict rock fragmentation through cascaded forward neural network (CFNN) and radial basis function neural network (RBFNN) models. In this regards, the CFNN was trained by the Levenberg-Marquardt algorithm (LMA) and Conjugate gradient backpropagation (CGP). Further, the RBFNN was optimized by the Dragonfly Algorithm (DA) and teaching-learning-based optimization (TLBO). For developing the models, the database required was collected from the Midouk copper mine, Iran. After modeling, the statistical functions were computed to check the accuracy of the models, and the root mean square errors (RMSEs) of CFNN-LMA, CFNN-CGP, RBFNN-DA, and RBFNN-TLBO were obtained as 1.0656, 1.9698, 2.2235, and 1.6216, respectively. Accordingly, CFNN-LMA, with the lowest RMSE, was determined as the model with the best prediction results among the four examined in this study.

• 화약ㆍ발파
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• 제35권1호
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• pp.18-26
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• 2017

발파암의 파쇄도 분석에는 이미지 엣지 검출기법을 응용한 사진분석 방법이 주로 사용되어 왔으며, 이들 이미지 획득은 주로 파쇄암의 정면에서 디지털 이미지로 획득하였다. 그러나 기본적으로 이미지 분석은 정면 촬영이 아닌 평면 촬영 이미지를 이용하게 되어 있으나, 거대한 암반 사면을 평면 촬영할 수 있는 수단이 없었다. 따라서 부득이하게 정면 촬영된 이미지를 임의 왜곡 또는 확대하여 평면 촬영 각도와 유사하게 조절함으로서 해결하였다. 근래에 이르러 무인항공기(UAV)가 발전하면서 이를 통해 발파암의 파쇄 상황을 간단히 고화질 디지털 이미지화 할 수 있게 되었고, 이를 통해 파쇄암이 쌓여있는 각도에 최대한 평면인 이미지를 획득하고 이미지 분석을 할 수 있게 되었다. 본 연구는 무인항공기와 무인항공기용 카메라를 이용해 발파 파쇄암의 정면 및 평면 디지털 이미지를 동시에 획득하고 각각을 비교 분석하였다. 그 결과 평면 촬영된 이미지의 분석 결과가 기존 정면 촬영된 이미지의 분석결과에 비해 정확도가 크게 향상되었음을 확인하였다.

• Geomechanics and Engineering
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• 제34권5호
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• pp.547-559
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• 2023

We evaluated the applicability of machine learning techniques and the Kuz-Ram model for predicting the mean fragmentation size in open-pit mines. The characteristics of the in-situ rock considered here were uniaxial compressive strength, tensile strength, rock factor, and mean in-situ block size. Seventy field datasets that included these characteristics were collected to predict the mean fragmentation size. Deep neural network, support vector machine, and extreme gradient boosting (XGBoost) models were trained using the data. The performance was evaluated using the root mean squared error (RMSE) and the coefficient of determination (r²). The XGBoost model had the smallest RMSE and the highest r² value compared with the other models. Additionally, when analyzing the error rate between the measured and predicted values, XGBoost had the lowest error rate. When the Kuz-Ram model was applied, low accuracy was observed owing to the differences in the characteristics of data used for model development. Consequently, the proposed XGBoost model predicted the mean fragmentation size more accurately than other models. If its performance is improved by securing sufficient data in the future, it will be useful for improving the blasting efficiency at the target site.