Application of Cascade Forward Backpropagation Neural Networks for Selecting Mining Methods

Ahmed M. A. Shohda, Mahrous A. M. Ali, Gaofeng Ren, Jong-Gwan Kim and Mohamed Abd-El-Hakeem Mohamed
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Ahmed M. A. Shohda: School of Resources and Environmental Engineering, Wuhan University of Technology, Luoshi Road 122, Wuhan 430070, China
Mahrous A. M. Ali: Mining and Petroleum Engineering Department, Faculty of Engineering-Qena, Al-Azhar University, Cairo 83511, Egypt
Gaofeng Ren: School of Resources and Environmental Engineering, Wuhan University of Technology, Luoshi Road 122, Wuhan 430070, China
Jong-Gwan Kim: Department of Energy and Resources Engineering, Chonnam National University, Gwangju 61186, Korea
Mohamed Abd-El-Hakeem Mohamed: Electric Department, Faculty of Engineering-Qena, Al-Azhar University, Cairo 83511, Egypt

Sustainability, 2022, vol. 14, issue 2, 1-14

Abstract: Decision-making is very important in many fields, such as mining engineering. In addition, there has been a growth of computer applications in all fields, especially mining operations. One of these application fields is mine design and the selection of suitable mining methods, and computer applications can help mine engineers to decide upon and choose more satisfactory methods. The selection of mining methods depends on the rock-layer specification. All rock characteristics should be classified in terms of technical and economic concerns related to mining rock specifications, such as mechanical and physical properties, and evaluated according to their weights and ratings. Methodologically, in this study, the criteria considered in the University of British Columbia (UBC) method were used as references to establish general criteria. These criteria consist of general shape, ore thickness, ore plunge, and grade distribution, in addition to the rock quality designation (ore zone, hanging wall, and foot wall) and rock substance strength (ore zone, hanging wall, and foot wall). The technique for order of preference by similarity to ideal solution (TOPSIS) was adopted, and an improved TOPSIS method was developed based on experimental testing and checked by means of the application of cascade forward backpropagation neural networks in mining method selection. The results provide indicators that decision makers can use to choose between different mining methods based on the total points given to all ore properties. The best mining method is cut and fill stopping, with a rank of 0.70, and the second is top slicing, with a rank of 0.67.

Keywords: TOPSIS; neural networks; cascade forward backpropagation; mining method selection; multiple-criterion decision making (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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