Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithms
Blasting is well-known as an effective method for fragmenting or moving rock in open-pit mines. To evaluate the quality of blasting, the size of rock distribution is used as a critical criterion in blasting operations. A high percentage of oversized rocks generated by blasting operations can lead to...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-05-01
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| Series: | Geoscience Frontiers |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1674987120302486 |
| _version_ | 1797707793756585984 |
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| author | Chengyu Xie Hoang Nguyen Xuan-Nam Bui Yosoon Choi Jian Zhou Thao Nguyen-Trang |
| author_facet | Chengyu Xie Hoang Nguyen Xuan-Nam Bui Yosoon Choi Jian Zhou Thao Nguyen-Trang |
| author_sort | Chengyu Xie |
| collection | DOAJ |
| description | Blasting is well-known as an effective method for fragmenting or moving rock in open-pit mines. To evaluate the quality of blasting, the size of rock distribution is used as a critical criterion in blasting operations. A high percentage of oversized rocks generated by blasting operations can lead to economic and environmental damage. Therefore, this study proposed four novel intelligent models to predict the size of rock distribution in mine blasting in order to optimize blasting parameters, as well as the efficiency of blasting operation in open mines. Accordingly, a nature-inspired algorithm (i.e., firefly algorithm – FFA) and different machine learning algorithms (i.e., gradient boosting machine (GBM), support vector machine (SVM), Gaussian process (GP), and artificial neural network (ANN)) were combined for this aim, abbreviated as FFA-GBM, FFA-SVM, FFA-GP, and FFA-ANN, respectively. Subsequently, predicted results from the abovementioned models were compared with each other using three statistical indicators (e.g., mean absolute error, root-mean-squared error, and correlation coefficient) and color intensity method. For developing and simulating the size of rock in blasting operations, 136 blasting events with their images were collected and analyzed by the Split-Desktop software. In which, 111 events were randomly selected for the development and optimization of the models. Subsequently, the remaining 25 blasting events were applied to confirm the accuracy of the proposed models. Herein, blast design parameters were regarded as input variables to predict the size of rock in blasting operations. Finally, the obtained results revealed that the FFA is a robust optimization algorithm for estimating rock fragmentation in bench blasting. Among the models developed in this study, FFA-GBM provided the highest accuracy in predicting the size of fragmented rocks. The other techniques (i.e., FFA-SVM, FFA-GP, and FFA-ANN) yielded lower computational stability and efficiency. Hence, the FFA-GBM model can be used as a powerful and precise soft computing tool that can be applied to practical engineering cases aiming to improve the quality of blasting and rock fragmentation. |
| first_indexed | 2024-03-12T06:11:41Z |
| format | Article |
| id | doaj.art-6c9645fa576846f384db0e1546422cd8 |
| institution | Directory Open Access Journal |
| issn | 1674-9871 |
| language | English |
| last_indexed | 2024-03-12T06:11:41Z |
| publishDate | 2021-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Geoscience Frontiers |
| spelling | doaj.art-6c9645fa576846f384db0e1546422cd82023-09-03T02:54:47ZengElsevierGeoscience Frontiers1674-98712021-05-01123101108Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithmsChengyu Xie0Hoang Nguyen1Xuan-Nam Bui2Yosoon Choi3Jian Zhou4Thao Nguyen-Trang5School of Environment and Resources, Xiangtan University, Xiangtan 411105, ChinaInstitute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam; Corresponding author.Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, 18 Pho Vien, Duc Thang ward, Bac Tu Liem district, Hanoi 100000, Viet Nam; Center for Mining, Electro-Mechanical research, Hanoi University of Mining and Geology, 18 Pho Vien, Duc Thang ward, Bac Tu Liem district, Hanoi 100000, Viet NamDepartment of Energy Resources Engineering, Pukyong National University, Busan 48513, Republic of KoreaSchool of Resources and Safety Engineering, Central South University, Changsha 410083, Hunan, ChinaDivision of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City 700000, Viet Nam; Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City 700000, Viet NamBlasting is well-known as an effective method for fragmenting or moving rock in open-pit mines. To evaluate the quality of blasting, the size of rock distribution is used as a critical criterion in blasting operations. A high percentage of oversized rocks generated by blasting operations can lead to economic and environmental damage. Therefore, this study proposed four novel intelligent models to predict the size of rock distribution in mine blasting in order to optimize blasting parameters, as well as the efficiency of blasting operation in open mines. Accordingly, a nature-inspired algorithm (i.e., firefly algorithm – FFA) and different machine learning algorithms (i.e., gradient boosting machine (GBM), support vector machine (SVM), Gaussian process (GP), and artificial neural network (ANN)) were combined for this aim, abbreviated as FFA-GBM, FFA-SVM, FFA-GP, and FFA-ANN, respectively. Subsequently, predicted results from the abovementioned models were compared with each other using three statistical indicators (e.g., mean absolute error, root-mean-squared error, and correlation coefficient) and color intensity method. For developing and simulating the size of rock in blasting operations, 136 blasting events with their images were collected and analyzed by the Split-Desktop software. In which, 111 events were randomly selected for the development and optimization of the models. Subsequently, the remaining 25 blasting events were applied to confirm the accuracy of the proposed models. Herein, blast design parameters were regarded as input variables to predict the size of rock in blasting operations. Finally, the obtained results revealed that the FFA is a robust optimization algorithm for estimating rock fragmentation in bench blasting. Among the models developed in this study, FFA-GBM provided the highest accuracy in predicting the size of fragmented rocks. The other techniques (i.e., FFA-SVM, FFA-GP, and FFA-ANN) yielded lower computational stability and efficiency. Hence, the FFA-GBM model can be used as a powerful and precise soft computing tool that can be applied to practical engineering cases aiming to improve the quality of blasting and rock fragmentation.http://www.sciencedirect.com/science/article/pii/S1674987120302486Mine blastingRock fragmentationArtificial intelligenceHybrid modelGradient boosting machineMeta-heuristic algorithm |
| spellingShingle | Chengyu Xie Hoang Nguyen Xuan-Nam Bui Yosoon Choi Jian Zhou Thao Nguyen-Trang Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithms Geoscience Frontiers Mine blasting Rock fragmentation Artificial intelligence Hybrid model Gradient boosting machine Meta-heuristic algorithm |
| title | Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithms |
| title_full | Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithms |
| title_fullStr | Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithms |
| title_full_unstemmed | Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithms |
| title_short | Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithms |
| title_sort | predicting rock size distribution in mine blasting using various novel soft computing models based on meta heuristics and machine learning algorithms |
| topic | Mine blasting Rock fragmentation Artificial intelligence Hybrid model Gradient boosting machine Meta-heuristic algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S1674987120302486 |
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