Open-Pit Bench Blasting Fragmentation Prediction Based on Stacking Integrated Strategy
The size distribution of rock fragments significantly influences subsequent operations in geotechnical and mining engineering projects. Thus, accurate prediction of this distribution according to the relevant blasting design parameters is essential. This study employs artificial intelligence methods...
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MDPI AG
2025-01-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/3/1254 |
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| author | Yikun Sui Zhiyong Zhou Rui Zhao Zheng Yang Yang Zou |
| author_facet | Yikun Sui Zhiyong Zhou Rui Zhao Zheng Yang Yang Zou |
| author_sort | Yikun Sui |
| collection | DOAJ |
| description | The size distribution of rock fragments significantly influences subsequent operations in geotechnical and mining engineering projects. Thus, accurate prediction of this distribution according to the relevant blasting design parameters is essential. This study employs artificial intelligence methods to predict the fragmentation of open-pit bench blasting. The study employed a dataset comprising 97 blast fragment samples. Random forest and XGBoost models were utilized as base learners. A prediction model was developed using the stacking integrated strategy to enhance predictive performance. The model’s performance was evaluated using the coefficient of determination (<i>R</i><sup>2</sup>), the mean square error (MSE), the root mean square error (RMSE), and the mean absolute error (MAE). The results indicated that the model achieved the highest prediction accuracy, with an <i>R</i><sup>2</sup> of 0.943. In the training set, the model achieved MSE, RMSE, and MAE values of 0.00269, 0.05187, and 0.03320, while in the testing set, these values were 0.00197, 0.04435, and 0.03687, respectively. The model was validated using five sets of actual blasting block data from a northeastern mining area, which yielded more accurate prediction results. These findings demonstrate that the stacking strategy effectively enhances the prediction performance of a single model and offers innovative approaches to predicting blasting block size. |
| format | Article |
| id | doaj-art-55317baa80c8476894849edaa3db09db |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-55317baa80c8476894849edaa3db09db2025-08-20T02:48:01ZengMDPI AGApplied Sciences2076-34172025-01-01153125410.3390/app15031254Open-Pit Bench Blasting Fragmentation Prediction Based on Stacking Integrated StrategyYikun Sui0Zhiyong Zhou1Rui Zhao2Zheng Yang3Yang Zou4School of Resource and Safety Engineering, Central South University, Changsha 410083, ChinaSchool of Resource and Safety Engineering, Central South University, Changsha 410083, ChinaSchool of Resource and Safety Engineering, Central South University, Changsha 410083, ChinaSchool of Resource and Safety Engineering, Central South University, Changsha 410083, ChinaDivision of Mining and Geotechnical Engineering, Luleå University of Technology, 97187 Luleå, SwedenThe size distribution of rock fragments significantly influences subsequent operations in geotechnical and mining engineering projects. Thus, accurate prediction of this distribution according to the relevant blasting design parameters is essential. This study employs artificial intelligence methods to predict the fragmentation of open-pit bench blasting. The study employed a dataset comprising 97 blast fragment samples. Random forest and XGBoost models were utilized as base learners. A prediction model was developed using the stacking integrated strategy to enhance predictive performance. The model’s performance was evaluated using the coefficient of determination (<i>R</i><sup>2</sup>), the mean square error (MSE), the root mean square error (RMSE), and the mean absolute error (MAE). The results indicated that the model achieved the highest prediction accuracy, with an <i>R</i><sup>2</sup> of 0.943. In the training set, the model achieved MSE, RMSE, and MAE values of 0.00269, 0.05187, and 0.03320, while in the testing set, these values were 0.00197, 0.04435, and 0.03687, respectively. The model was validated using five sets of actual blasting block data from a northeastern mining area, which yielded more accurate prediction results. These findings demonstrate that the stacking strategy effectively enhances the prediction performance of a single model and offers innovative approaches to predicting blasting block size.https://www.mdpi.com/2076-3417/15/3/1254blasting fragmentation predictionstacking integrated strategyrandom forestXGBoost |
| spellingShingle | Yikun Sui Zhiyong Zhou Rui Zhao Zheng Yang Yang Zou Open-Pit Bench Blasting Fragmentation Prediction Based on Stacking Integrated Strategy Applied Sciences blasting fragmentation prediction stacking integrated strategy random forest XGBoost |
| title | Open-Pit Bench Blasting Fragmentation Prediction Based on Stacking Integrated Strategy |
| title_full | Open-Pit Bench Blasting Fragmentation Prediction Based on Stacking Integrated Strategy |
| title_fullStr | Open-Pit Bench Blasting Fragmentation Prediction Based on Stacking Integrated Strategy |
| title_full_unstemmed | Open-Pit Bench Blasting Fragmentation Prediction Based on Stacking Integrated Strategy |
| title_short | Open-Pit Bench Blasting Fragmentation Prediction Based on Stacking Integrated Strategy |
| title_sort | open pit bench blasting fragmentation prediction based on stacking integrated strategy |
| topic | blasting fragmentation prediction stacking integrated strategy random forest XGBoost |
| url | https://www.mdpi.com/2076-3417/15/3/1254 |
| work_keys_str_mv | AT yikunsui openpitbenchblastingfragmentationpredictionbasedonstackingintegratedstrategy AT zhiyongzhou openpitbenchblastingfragmentationpredictionbasedonstackingintegratedstrategy AT ruizhao openpitbenchblastingfragmentationpredictionbasedonstackingintegratedstrategy AT zhengyang openpitbenchblastingfragmentationpredictionbasedonstackingintegratedstrategy AT yangzou openpitbenchblastingfragmentationpredictionbasedonstackingintegratedstrategy |