• Tunnel and Underground Space
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• v.14 no.5
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• pp.353-362
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• 2004

Prediction of fragmentation in bench blasting is one of the most important factors to establish the production plan. It is widely accepted that fragmentation could be accurately predicted using the Kuz-Ram model in bench blasting. Nevertheless, the model has an ambiguous or subjective aspect in evaluating the model parameters such as joint condition, rock strength, density, burden, explosive strength and spacing. This study proposes a new method to evaluate the parameters of Kuz-Ram model, and the predicted mean fragment sizes using the proposed method are examined by comparing the measured sizes in the field. The results show that the predictions using Kuz-Ram model with the proposed method coincide with field measurements, but Kuz-Ram model does not reflect the in-situ rock condition and hence needs to be improved.

• Tunnel and Underground Space
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• v.26 no.2
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• pp.120-130
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• 2016

Considering the applicability of Kuz-Ram model, which has been used extensively for predicting rock fragmentation size distribution by blasting, to domestic open-pit limestone mine, a total of 21 blasting tests have been executed at an open-pit limestone mine in eastern Gangwon of South Korea. A comparative analysis of field measured value and Kuz-Ram predicted value showed that there are a considerable amount of error in the predicted values regardless of application of various correction parameters for rock factor and uniformity factor; up to 56.45% in mean fragmentation size and 37.52% in uniformity index. Also the problem of applying different correction parameters has been derived even though a similar blasting pattern has been adopted for a same blasting bench. The authors therefore suggest that Kuz-Ram model needs to be modified for a proper application to domestic open-pit limestone mine.

• Geomechanics and Engineering
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• v.34 no.5
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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.

• Tunnel and Underground Space
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• v.15 no.4 s.57
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• pp.264-274
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• 2005

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 levels is provided to estimate production costs for each process and to make a plan for an optimum blasting pattern.

• 지반과기술
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• v.3 no.3
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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.