Review on the Development of Mining Method Selection to Identify New Techniques Using a Cascade-Forward Backpropagation Neural Network
The most crucial event in a mining project is the selection of an appropriate mining method (MMS). Consequently, determining the optimal choice is critical because it impacts most of the other key decisions. This study provides a concise overview of the development of multiple selection methods usin...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/6952492 |
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| author | Mohamed E. I. Abdelrasoul Guangjin Wang Jong-Gwan Kim Gaofeng Ren Mohamed Abd-El-Hakeem Mohamed Mahrous A. M. Ali Wael R. Abdellah |
| author_facet | Mohamed E. I. Abdelrasoul Guangjin Wang Jong-Gwan Kim Gaofeng Ren Mohamed Abd-El-Hakeem Mohamed Mahrous A. M. Ali Wael R. Abdellah |
| author_sort | Mohamed E. I. Abdelrasoul |
| collection | DOAJ |
| description | The most crucial event in a mining project is the selection of an appropriate mining method (MMS). Consequently, determining the optimal choice is critical because it impacts most of the other key decisions. This study provides a concise overview of the development of multiple selection methods using a cascade-forward backpropagation neural network (CFBPNN). Numerous methods of multicriteria decision-making (MCDM) are discussed and compared herein. The comparison includes several factors, such as applicability, subjectivity, qualitative and quantitative data, sensitivity, and validity. The application of artificial intelligence is presented and discussed using CFBPNN. The Chengchao iron mine was selected for this investigation to pick the optimum mining method. The results revealed that cut and fill stoping is the most appropriate mining method, followed by sublevel and shrinkage stoping methods. The least appropriate method is open-pit mining, followed by room and pillar and longwall mining methods. |
| format | Article |
| id | doaj-art-fa8a2f4ccbc04c398ba6dd7fd9ceb17a |
| institution | OA Journals |
| issn | 1687-8094 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-fa8a2f4ccbc04c398ba6dd7fd9ceb17a2025-08-20T02:22:37ZengWileyAdvances in Civil Engineering1687-80942022-01-01202210.1155/2022/6952492Review on the Development of Mining Method Selection to Identify New Techniques Using a Cascade-Forward Backpropagation Neural NetworkMohamed E. I. Abdelrasoul0Guangjin Wang1Jong-Gwan Kim2Gaofeng Ren3Mohamed Abd-El-Hakeem Mohamed4Mahrous A. M. Ali5Wael R. Abdellah6Faculty of Land Resources EngineeringFaculty of Land Resources EngineeringDepartment of Energy and Resources EngineeringWuhan University of TechnologyElectric DepartmentMining and Petroleum Engineering DepartmentDepartment of Mining and Metallurgical EngineeringThe most crucial event in a mining project is the selection of an appropriate mining method (MMS). Consequently, determining the optimal choice is critical because it impacts most of the other key decisions. This study provides a concise overview of the development of multiple selection methods using a cascade-forward backpropagation neural network (CFBPNN). Numerous methods of multicriteria decision-making (MCDM) are discussed and compared herein. The comparison includes several factors, such as applicability, subjectivity, qualitative and quantitative data, sensitivity, and validity. The application of artificial intelligence is presented and discussed using CFBPNN. The Chengchao iron mine was selected for this investigation to pick the optimum mining method. The results revealed that cut and fill stoping is the most appropriate mining method, followed by sublevel and shrinkage stoping methods. The least appropriate method is open-pit mining, followed by room and pillar and longwall mining methods.http://dx.doi.org/10.1155/2022/6952492 |
| spellingShingle | Mohamed E. I. Abdelrasoul Guangjin Wang Jong-Gwan Kim Gaofeng Ren Mohamed Abd-El-Hakeem Mohamed Mahrous A. M. Ali Wael R. Abdellah Review on the Development of Mining Method Selection to Identify New Techniques Using a Cascade-Forward Backpropagation Neural Network Advances in Civil Engineering |
| title | Review on the Development of Mining Method Selection to Identify New Techniques Using a Cascade-Forward Backpropagation Neural Network |
| title_full | Review on the Development of Mining Method Selection to Identify New Techniques Using a Cascade-Forward Backpropagation Neural Network |
| title_fullStr | Review on the Development of Mining Method Selection to Identify New Techniques Using a Cascade-Forward Backpropagation Neural Network |
| title_full_unstemmed | Review on the Development of Mining Method Selection to Identify New Techniques Using a Cascade-Forward Backpropagation Neural Network |
| title_short | Review on the Development of Mining Method Selection to Identify New Techniques Using a Cascade-Forward Backpropagation Neural Network |
| title_sort | review on the development of mining method selection to identify new techniques using a cascade forward backpropagation neural network |
| url | http://dx.doi.org/10.1155/2022/6952492 |
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