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Geomechanics and Engineering Volume 34, Number 5, September10 2023 , pages 547-559 DOI: https://doi.org/10.12989/gae.2023.34.5.547 |
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Mean fragmentation size prediction in an open-pit mine using machine learning techniques and the Kuz-Ram model |
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Seung-Joong Lee and Sung-Oong Choi
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| Abstract | ||
| 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. | ||
| Key Words | ||
| deep neural network; extreme gradient boosting; machine learning; mean fragmentation size; support vector machine | ||
| Address | ||
| Seung-Joong Lee: Infra Solution Business Team, Hanwha Corporation / Global, Cheonggyecheon-ro 86, Jung-gu, Seoul, Republic of Korea Sung-Oong Choi: Department of Energy and Resources Engineering, Kangwon National University, Gangwondaehakgil 1,Chuncheon-si, Gangwon-do, Republic of Korea | ||