A New Model Based on Artificial Neural Networks and Game Theory for the Selection of Underground Mining Method

Özyurt, Meriç Can; Karadoğan, Abdulkadir

doi:10.1134/s1062739120016491

Cited by 13 publications

(8 citation statements)

References 12 publications

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“…Similarly, Chen and Shiaxiang [22] trained (and tested) a regression ANN model for evaluating and predicting the most optimum underground mining method (and panel production per day) to recover thin coal seams based on samples from mining case studies and six input factors. Ozyurt and Karadogan [23] went further investigating the applicability of ANN and game theory to develop a model for underground MMS for different ore types. They developed six ANN models in their study to evaluate orebody geometry, rock mass properties, environmental factors, and ventilation conditions to determine the most optimum mining methods (eleven methods ranked based on safety).…”

Section: Mining Methods Selection Systemsmentioning

confidence: 99%

“…Thereafter, the most optimum mining method was selected by applying the ultimatum game theory, i.e., players (decision-makers) have a task to select the most optimum mining method. The ANN models by Ozyurt and Karadogan [23] were trained based on synthetic data and tested using actual sample data from mining case studies with a total of around nineteen input parameters. According to the authors, their model can be used even when information about the required input factors is missing.…”

Section: Mining Methods Selection Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Applying Nonnegative Matrix Factorization for Underground Mining Method Selection Based on Mining Projects' Historical Data

MANJATE,

OHTOMO,

ARIMA

et al. 2023

Int.J.Soc.Mater.Eng.Resour.

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Mining methods selection (MMS) is one of the most critical and complex decision-making tasks in mine planning. The selection of underground mining methods is considered to be the most problematic due to the complexity associated with the orebody geometry, geology, and geotechnical properties. This study integrated artificial intelligence and machine learning in the MMS process by introducing the recommendation systems (RS) approach in MMS through the nonnegative matrix factorization (NMF) algorithm. As such, the weighted nonnegative matrix factorization (WNMF) algorithm is applied to build a model for underground MMS. The study's input dataset is based on thirty mining projects' historical data. In the experiments, we evaluate the capability of the WNMF to predict underground mining methods using five input variables: ore strength, host-rock strength, orebody thickness, shape, and dip. The results show that the WNMF model achieved an average prediction accuracy of 67.5%, considered reasonable and realistic. Further findings reveal that the WNMF model is sensitive to the imbalanced class dataset used in the experiments, thus, suggesting the need to improve the dataset's quality. These results reveal the model's effectiveness in predicting underground mining methods; therefore, with continuous improvement, the WNMF model can be effectively applied in underground MMS.

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Section: Mining Methods Selection Systemsmentioning

confidence: 99%

Section: Mining Methods Selection Systemsmentioning

confidence: 99%

Applying Nonnegative Matrix Factorization for Underground Mining Method Selection Based on Mining Projects' Historical Data

MANJATE,

OHTOMO,

ARIMA

et al. 2023

Int.J.Soc.Mater.Eng.Resour.

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show abstract

“…In recent years, a plethora of mining engineering problems have been resolved through the application of ANNs. Ozyurt et al 23 developed six different ANN models and investigated the applicability of ANNs and game theory in the development of an underground mining method selection model. Yu and Ren 24 devised a GA-BP network image recognition model to contrast and choose multiple approaches for production blasting design, providing a quantitative basis for the rational selection of production blasting design parameters.…”

Section: Introductionmentioning

confidence: 99%

Mining Technology Evaluation for Steep Coal Seams Based on a GA-BP Neural Network

Li,

Wang,

et al. 2024

ACS Omega

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Many mines in Guizhou Province are in urgent need of renovation to ensure harmonious operation and prolong their lifespan. The key to successful renovation lies in the prudent selection of the appropriate mining technologies. Therefore, a comprehensive investigation was conducted on steep coal mines in Guizhou Province, and a comprehensive evaluation framework was established. Spearman correlation analysis was performed on various factors, selecting geological conditions and working face parameters with high correlation as the input variables and mining methods as the output variables. The optimal values of each hyperparameter were determined through orthogonal experiments, and the neural network structure was confirmed to be “17-9-3”. Five variants of backpropagation (BP) algorithms were meticulously tested, and a genetic algorithm optimizing the BP neural network (GA-BP) was further assessed to improve the model’s prediction accuracy. The accuracy of the model was evaluated via the coefficient of determination (R 2) and mean squared error (MSE). The research results indicated that the variable step–size algorithm with a momentum term (VSS + MT) was the optimal algorithm for the BP neural network. Additionally, the MSE values of the artificial neural network and GA-BP neural network in the testing phase were 0.06 and 0.04, with prediction success rates of 70 and 90%, respectively, and R 2 values of 0.79 and 0.85, respectively. Thus, the GA-BP neural network demonstrated superior performance. Finally, industrial application of the model was conducted on a working face in the Zhong-Yu coal mine. The evaluation index for the working face was “0.847, 0.09, 0.111”, suggesting that fully mechanized mining should be adopted. The evaluation results were consistent with the current production status of the mine, verifying the reliability of the model in practical applications.

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“…Cheng et al optimized an airflow system in an underground mining facility [21]. Ozyurt (2018) stated that ANNs, which are computer programs that offer answers for comparable or specific cases (regardless of the shortage of information) by learning from reason and impact relationships in pattern cases, can overcome the abovementioned problems [22]. Yang and Zhang (1997) and Lv and Zhang (2014) employed ANNs to choose the most suitable mining technique for a mine deposit [23,24].…”

Section: Introductionmentioning

confidence: 99%

“…This model could make predictions despite a loss of facts by means of the following technological traits and new findings received in scientific/sectoral research if learning is continuous. Moreover, the model can examine all choice standards and offer primarily literature-based solutions [25]. Lawal and Musa applied an artificial neural network (ANN)-based mathematical model for the prediction of blast-induced ground vibrations [26].…”

Section: Introductionmentioning

confidence: 99%

Application of Cascade Forward Backpropagation Neural Networks for Selecting Mining Methods

et al. 2022

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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.

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A New Model Based on Artificial Neural Networks and Game Theory for the Selection of Underground Mining Method

Cited by 13 publications

References 12 publications

Applying Nonnegative Matrix Factorization for Underground Mining Method Selection Based on Mining Projects' Historical Data

Applying Nonnegative Matrix Factorization for Underground Mining Method Selection Based on Mining Projects' Historical Data

Mining Technology Evaluation for Steep Coal Seams Based on a GA-BP Neural Network

Application of Cascade Forward Backpropagation Neural Networks for Selecting Mining Methods

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