Application of Soft Computing, Statistical and Multi-Criteria Decision-Making Methods to Develop a Predictive Equation for Prediction of Flyrock Distance in Open-Pit Mining
Blasting operations in open-pit mines generally have various management strategies relating to flyrock. There are empirical models for calculating the flyrock distance, but due to the complexity and uncertainty of rock properties and their interactions with blasting properties, there are still no mo...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Mining |
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| Online Access: | https://www.mdpi.com/2673-6489/3/2/19 |
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| author | Mohammad Babaeian Farhang Sereshki Mohammad Ataei Micah Nehring Sadjad Mohammadi |
| author_facet | Mohammad Babaeian Farhang Sereshki Mohammad Ataei Micah Nehring Sadjad Mohammadi |
| author_sort | Mohammad Babaeian |
| collection | DOAJ |
| description | Blasting operations in open-pit mines generally have various management strategies relating to flyrock. There are empirical models for calculating the flyrock distance, but due to the complexity and uncertainty of rock properties and their interactions with blasting properties, there are still no models that can predict the flyrock distance that may be applicable across mining operations in general. In this regard, the Jajarm bauxite mine complex was used as a case study. The purpose of this study was to develop and evaluate different methods that can predict flyrock distance. For this purpose, soft computing models were developed using generalized regression neural network (GRNN), gene expression programming (GEP) and genetic-algorithm-based GRNN (GA-GRNN) methods. To obtain statistical models, multivariable regression was applied in the form of linear and nonlinear equations. A flyrock index was introduced using a classification system developed by incorporating fuzzy decision-making trial and evaluation methods (fuzzy DEMATEL). In order to achieve this goal, the data of 118 blasts in eight mines of the Jajarm bauxite complex were collected and used. Following this, four performance benchmarks were applied: the coefficient of determination (R<sup>2</sup>), variance accounted for (VAF), root-mean-square error (RMSE) and mean absolute percentage error (MAPE). The performance of the models was evaluated, and they were compared with each other as well as with the most common previous empirical models. The obtained results indicate that the GA-GRNN model has a higher performance in predicting the flyrock distance in actual cases compared to the other models. At first, data on factors that were the main cause of flyrock (and had a direct impact on it) were collected and classified from different blasts. Then, using the collected data, 19 different combinations were established, which can be used to provide the appropriate predictive equation. The purpose of this work is to more accurately predict flyrock and prevent heavy damage to buildings and mining machines across the mining complex. |
| first_indexed | 2024-03-11T02:06:54Z |
| format | Article |
| id | doaj.art-1204d35816d34951be1db301a0d4f959 |
| institution | Directory Open Access Journal |
| issn | 2673-6489 |
| language | English |
| last_indexed | 2024-03-11T02:06:54Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mining |
| spelling | doaj.art-1204d35816d34951be1db301a0d4f9592023-11-18T11:46:18ZengMDPI AGMining2673-64892023-05-013230433310.3390/mining3020019Application of Soft Computing, Statistical and Multi-Criteria Decision-Making Methods to Develop a Predictive Equation for Prediction of Flyrock Distance in Open-Pit MiningMohammad Babaeian0Farhang Sereshki1Mohammad Ataei2Micah Nehring3Sadjad Mohammadi4School of Mining Engineering, Petroleum and Geophysics, Shahrood University of Technology, Shahrood 3619995161, IranSchool of Mining Engineering, Petroleum and Geophysics, Shahrood University of Technology, Shahrood 3619995161, IranSchool of Mining Engineering, Petroleum and Geophysics, Shahrood University of Technology, Shahrood 3619995161, IranSchool of Mechanical and Mining Engineering, University of Queensland, Brisbane, QLD 4072, AustraliaSchool of Mining Engineering, Petroleum and Geophysics, Shahrood University of Technology, Shahrood 3619995161, IranBlasting operations in open-pit mines generally have various management strategies relating to flyrock. There are empirical models for calculating the flyrock distance, but due to the complexity and uncertainty of rock properties and their interactions with blasting properties, there are still no models that can predict the flyrock distance that may be applicable across mining operations in general. In this regard, the Jajarm bauxite mine complex was used as a case study. The purpose of this study was to develop and evaluate different methods that can predict flyrock distance. For this purpose, soft computing models were developed using generalized regression neural network (GRNN), gene expression programming (GEP) and genetic-algorithm-based GRNN (GA-GRNN) methods. To obtain statistical models, multivariable regression was applied in the form of linear and nonlinear equations. A flyrock index was introduced using a classification system developed by incorporating fuzzy decision-making trial and evaluation methods (fuzzy DEMATEL). In order to achieve this goal, the data of 118 blasts in eight mines of the Jajarm bauxite complex were collected and used. Following this, four performance benchmarks were applied: the coefficient of determination (R<sup>2</sup>), variance accounted for (VAF), root-mean-square error (RMSE) and mean absolute percentage error (MAPE). The performance of the models was evaluated, and they were compared with each other as well as with the most common previous empirical models. The obtained results indicate that the GA-GRNN model has a higher performance in predicting the flyrock distance in actual cases compared to the other models. At first, data on factors that were the main cause of flyrock (and had a direct impact on it) were collected and classified from different blasts. Then, using the collected data, 19 different combinations were established, which can be used to provide the appropriate predictive equation. The purpose of this work is to more accurately predict flyrock and prevent heavy damage to buildings and mining machines across the mining complex.https://www.mdpi.com/2673-6489/3/2/19flyrockgene expression programming (GEP)generalized regression neural network (GRNN)genetic algorithm (GA)decision-making trial and evaluation methods (DEMATEL) |
| spellingShingle | Mohammad Babaeian Farhang Sereshki Mohammad Ataei Micah Nehring Sadjad Mohammadi Application of Soft Computing, Statistical and Multi-Criteria Decision-Making Methods to Develop a Predictive Equation for Prediction of Flyrock Distance in Open-Pit Mining Mining flyrock gene expression programming (GEP) generalized regression neural network (GRNN) genetic algorithm (GA) decision-making trial and evaluation methods (DEMATEL) |
| title | Application of Soft Computing, Statistical and Multi-Criteria Decision-Making Methods to Develop a Predictive Equation for Prediction of Flyrock Distance in Open-Pit Mining |
| title_full | Application of Soft Computing, Statistical and Multi-Criteria Decision-Making Methods to Develop a Predictive Equation for Prediction of Flyrock Distance in Open-Pit Mining |
| title_fullStr | Application of Soft Computing, Statistical and Multi-Criteria Decision-Making Methods to Develop a Predictive Equation for Prediction of Flyrock Distance in Open-Pit Mining |
| title_full_unstemmed | Application of Soft Computing, Statistical and Multi-Criteria Decision-Making Methods to Develop a Predictive Equation for Prediction of Flyrock Distance in Open-Pit Mining |
| title_short | Application of Soft Computing, Statistical and Multi-Criteria Decision-Making Methods to Develop a Predictive Equation for Prediction of Flyrock Distance in Open-Pit Mining |
| title_sort | application of soft computing statistical and multi criteria decision making methods to develop a predictive equation for prediction of flyrock distance in open pit mining |
| topic | flyrock gene expression programming (GEP) generalized regression neural network (GRNN) genetic algorithm (GA) decision-making trial and evaluation methods (DEMATEL) |
| url | https://www.mdpi.com/2673-6489/3/2/19 |
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