A Novel Approach for Blast-Induced Flyrock Prediction Based on Imperialist Competitive Algorithm and Artificial Neural Network
Flyrock is one of the major disturbances induced by blasting which may cause severe damage to nearby structures. This phenomenon has to be precisely predicted and subsequently controlled through the changing in the blast design to minimize potential risk of blasting. The scope of this study is to pr...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/643715 |
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| _version_ | 1849435358469554176 |
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| author | Aminaton Marto Mohsen Hajihassani Danial Jahed Armaghani Edy Tonnizam Mohamad Ahmad Mahir Makhtar |
| author_facet | Aminaton Marto Mohsen Hajihassani Danial Jahed Armaghani Edy Tonnizam Mohamad Ahmad Mahir Makhtar |
| author_sort | Aminaton Marto |
| collection | DOAJ |
| description | Flyrock is one of the major disturbances induced by blasting which may cause severe damage to nearby structures. This phenomenon has to be precisely predicted and subsequently controlled through the changing in the blast design to minimize potential risk of blasting. The scope of this study is to predict flyrock induced by blasting through a novel approach based on the combination of imperialist competitive algorithm (ICA) and artificial neural network (ANN). For this purpose, the parameters of 113 blasting operations were accurately recorded and flyrock distances were measured for each operation. By applying the sensitivity analysis, maximum charge per delay and powder factor were determined as the most influential parameters on flyrock. In the light of this analysis, two new empirical predictors were developed to predict flyrock distance. For a comparison purpose, a predeveloped backpropagation (BP) ANN was developed and the results were compared with those of the proposed ICA-ANN model and empirical predictors. The results clearly showed the superiority of the proposed ICA-ANN model in comparison with the proposed BP-ANN model and empirical approaches. |
| format | Article |
| id | doaj-art-e5578ff693a243b78ed753ccd0ac235a |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-e5578ff693a243b78ed753ccd0ac235a2025-08-20T03:26:20ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/643715643715A Novel Approach for Blast-Induced Flyrock Prediction Based on Imperialist Competitive Algorithm and Artificial Neural NetworkAminaton Marto0Mohsen Hajihassani1Danial Jahed Armaghani2Edy Tonnizam Mohamad3Ahmad Mahir Makhtar4Department of Geotechnics and Transportation, Universiti Teknologi Malaysia (UTM), 81310 Skudai, Johor, MalaysiaConstruction Research Alliance, Universiti Teknologi Malaysia (UTM), 81310 Skudai, Johor, MalaysiaDepartment of Geotechnics and Transportation, Universiti Teknologi Malaysia (UTM), 81310 Skudai, Johor, MalaysiaDepartment of Geotechnics and Transportation, Universiti Teknologi Malaysia (UTM), 81310 Skudai, Johor, MalaysiaDepartment of Structures and Materials, Universiti Teknologi Malaysia (UTM), 81310 Skudai, Johor, MalaysiaFlyrock is one of the major disturbances induced by blasting which may cause severe damage to nearby structures. This phenomenon has to be precisely predicted and subsequently controlled through the changing in the blast design to minimize potential risk of blasting. The scope of this study is to predict flyrock induced by blasting through a novel approach based on the combination of imperialist competitive algorithm (ICA) and artificial neural network (ANN). For this purpose, the parameters of 113 blasting operations were accurately recorded and flyrock distances were measured for each operation. By applying the sensitivity analysis, maximum charge per delay and powder factor were determined as the most influential parameters on flyrock. In the light of this analysis, two new empirical predictors were developed to predict flyrock distance. For a comparison purpose, a predeveloped backpropagation (BP) ANN was developed and the results were compared with those of the proposed ICA-ANN model and empirical predictors. The results clearly showed the superiority of the proposed ICA-ANN model in comparison with the proposed BP-ANN model and empirical approaches.http://dx.doi.org/10.1155/2014/643715 |
| spellingShingle | Aminaton Marto Mohsen Hajihassani Danial Jahed Armaghani Edy Tonnizam Mohamad Ahmad Mahir Makhtar A Novel Approach for Blast-Induced Flyrock Prediction Based on Imperialist Competitive Algorithm and Artificial Neural Network The Scientific World Journal |
| title | A Novel Approach for Blast-Induced Flyrock Prediction Based on Imperialist Competitive Algorithm and Artificial Neural Network |
| title_full | A Novel Approach for Blast-Induced Flyrock Prediction Based on Imperialist Competitive Algorithm and Artificial Neural Network |
| title_fullStr | A Novel Approach for Blast-Induced Flyrock Prediction Based on Imperialist Competitive Algorithm and Artificial Neural Network |
| title_full_unstemmed | A Novel Approach for Blast-Induced Flyrock Prediction Based on Imperialist Competitive Algorithm and Artificial Neural Network |
| title_short | A Novel Approach for Blast-Induced Flyrock Prediction Based on Imperialist Competitive Algorithm and Artificial Neural Network |
| title_sort | novel approach for blast induced flyrock prediction based on imperialist competitive algorithm and artificial neural network |
| url | http://dx.doi.org/10.1155/2014/643715 |
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