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.
Highlights
IntroductionMining method selection (MMS) is a time-consuming and difficult task that requires outstanding knowledge and experience
The TOPSIS technique has been employed as a multi-criterion decision-making method for sustainable selection among different mining methods, considering the layer specifications and its properties
Focused on linking all parameters related to all criteria in a simple manner and obtaining The Cascade forward Backpropagation Neural Network (CFBNN) method was applied in this research to select suitable mining methods accurate results
Summary
Mining method selection (MMS) is a time-consuming and difficult task that requires outstanding knowledge and experience. For correct and powerful evaluation, the decision maker may need to investigate a huge set of data and to consider many factors. This selection is vital for mine control because of the operational cost, as well as being an essential part of mine planning and design. Extensive research has been performed to discover an appropriate procedure for MMS This is a procedure for choosing an extraction approach for a specific deposit. It entails correct planning, research, and knowledgeable
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