Intelligence prediction of some selected environmental issues of blasting: A review
Background: Blasting is commonly used for loosening hard rock during excavation for generating the desired rock fragmentation required for optimizing the productivity of downstream operations. The environmental impacts resulting from such blasting operations include the generation of flyrock, ground...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Bentham Science Publishers
2020
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/91864/1/EdyTonnizamMohamad2020_IntelligencePredictionofSomeSelectedEnvironmentalIssues.pdf http://eprints.utm.my/id/eprint/91864/ http://dx.doi.org/10.2174/1874836802014010298 |
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| Institution: | Universiti Teknologi Malaysia |
| Language: | English |
| Summary: | Background: Blasting is commonly used for loosening hard rock during excavation for generating the desired rock fragmentation required for optimizing the productivity of downstream operations. The environmental impacts resulting from such blasting operations include the generation of flyrock, ground vibrations, air over pressure (AOp) and rock fragmentation. Objective: The purpose of this research is to evaluate the suitability of different computational techniques for the prediction of these environmental effects and to determine the key factors which contribute to each of these effects. This paper also identifies future research needs for the prediction of the environmental effects of blasting operations in hard rock. Methods: The various computational techniques utilized by the researchers in predicting blasting environmental issues such as artificial neural network (ANN), fuzzy interface system (FIS), imperialist competitive algorithm (ICA), and particle swarm optimization (PSO), were reviewed. Results: The results indicated that ANN, FIS and ANN-ICA were the best models for prediction of flyrock distance. FIS model was the best technique for the prediction of AOp and ground vibration. On the other hand, ANN was found to be the best for the assessment of fragmentation. Conclusion and Recommendation: It can be concluded that FIS, ANN-PSO, ANN-ICA models perform better than ANN models for the prediction of environmental issues of blasting using the same database. This paper further discusses how some of these techniques can be implemented by mining engineers and blasting team members at operating mines for predicting blast performance. |
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