Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization
Blasting is a major component of the construction and mining industries in terms of rock fragmentation and concrete demolition. Blast designers are constantly concerned about flyrock and ground vibration induced by blasting as adverse and unintended effects of explosive usage on the surrounding area...
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| Format: | Article |
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Springer-Verlag
2014
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| author | Armaghani, Danial Jahed Hajihassani, Mohsen Mohamad, Edy Tonnizam Marto, Aminaton Noorani, Seyed Ahmad |
| author_facet | Armaghani, Danial Jahed Hajihassani, Mohsen Mohamad, Edy Tonnizam Marto, Aminaton Noorani, Seyed Ahmad |
| author_sort | Armaghani, Danial Jahed |
| collection | ePrints |
| description | Blasting is a major component of the construction and mining industries in terms of rock fragmentation and concrete demolition. Blast designers are constantly concerned about flyrock and ground vibration induced by blasting as adverse and unintended effects of explosive usage on the surrounding areas. In recent years, several researches have been done to predict flyrock and ground vibration by means of conventional backpropagation (BP) artificial neural network (ANN). However, the convergence rate of the BP-ANN is relatively slow and solutions can be trapped at local minima. Since particle swarm optimization (PSO) is a robust global search algorithm, it can be used to improve ANNs' performance. In this study, a novel approach of incorporating PSO algorithm with ANN has been proposed to eliminate the limitation of the BP-ANN. This approach was applied to simulate the flyrock distance and peak particle velocity (PPV) induced by blasting. PSO parameters and optimal network architecture were determined using sensitivity analysis and trial and error method, respectively. Finally, a model was selected, and the proposed model was trained and tested using 44 datasets obtained from three granite quarry sites in Malaysia. Each dataset involved ten inputs, including the most influential parameters on flyrock distance and PPV, and two outputs. The results indicate that the proposed method is able to predict flyrock distance and PPV induced by blasting with a high degree of accuracy. Sensitivity analysis was also conducted to determine the influence of each parameter on flyrock distance and PPV. The results show that the powder factor and charge per delay are the most effective parameters on flyrock distance, whereas sub-drilling and charge per delay are the most effective parameters on PPV. |
| first_indexed | 2024-03-05T19:31:08Z |
| format | Article |
| id | utm.eprints-52026 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T19:31:08Z |
| publishDate | 2014 |
| publisher | Springer-Verlag |
| record_format | dspace |
| spelling | utm.eprints-520262018-11-30T07:00:23Z http://eprints.utm.my/52026/ Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization Armaghani, Danial Jahed Hajihassani, Mohsen Mohamad, Edy Tonnizam Marto, Aminaton Noorani, Seyed Ahmad TA Engineering (General). Civil engineering (General) Blasting is a major component of the construction and mining industries in terms of rock fragmentation and concrete demolition. Blast designers are constantly concerned about flyrock and ground vibration induced by blasting as adverse and unintended effects of explosive usage on the surrounding areas. In recent years, several researches have been done to predict flyrock and ground vibration by means of conventional backpropagation (BP) artificial neural network (ANN). However, the convergence rate of the BP-ANN is relatively slow and solutions can be trapped at local minima. Since particle swarm optimization (PSO) is a robust global search algorithm, it can be used to improve ANNs' performance. In this study, a novel approach of incorporating PSO algorithm with ANN has been proposed to eliminate the limitation of the BP-ANN. This approach was applied to simulate the flyrock distance and peak particle velocity (PPV) induced by blasting. PSO parameters and optimal network architecture were determined using sensitivity analysis and trial and error method, respectively. Finally, a model was selected, and the proposed model was trained and tested using 44 datasets obtained from three granite quarry sites in Malaysia. Each dataset involved ten inputs, including the most influential parameters on flyrock distance and PPV, and two outputs. The results indicate that the proposed method is able to predict flyrock distance and PPV induced by blasting with a high degree of accuracy. Sensitivity analysis was also conducted to determine the influence of each parameter on flyrock distance and PPV. The results show that the powder factor and charge per delay are the most effective parameters on flyrock distance, whereas sub-drilling and charge per delay are the most effective parameters on PPV. Springer-Verlag 2014 Article PeerReviewed Armaghani, Danial Jahed and Hajihassani, Mohsen and Mohamad, Edy Tonnizam and Marto, Aminaton and Noorani, Seyed Ahmad (2014) Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization. Arabian Journal of Geosciences, 7 (12). pp. 5383-5396. ISSN 1866-7511 http://dx.doi.org/10.1007/s12517-013-1174-0 DOI: 10.1007/s12517-013-1174-0 |
| spellingShingle | TA Engineering (General). Civil engineering (General) Armaghani, Danial Jahed Hajihassani, Mohsen Mohamad, Edy Tonnizam Marto, Aminaton Noorani, Seyed Ahmad Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization |
| title | Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization |
| title_full | Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization |
| title_fullStr | Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization |
| title_full_unstemmed | Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization |
| title_short | Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization |
| title_sort | blasting induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization |
| topic | TA Engineering (General). Civil engineering (General) |
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