Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization
Blasting is an inseparable part of the rock fragmentation process in hard rock mining. As an adverse and undesirable effect of blasting on surrounding areas, airblast-overpressure (AOp) is constantly considered by blast designers. AOp may impact the human and structures in adjacent to blasting area....
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my.utm.545582017-09-12T08:29:45Z http://eprints.utm.my/id/eprint/54558/ Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization Hajihassani, Mohsen Jahed Armaghani, Danial Sohaei, Houman Mohamad, Edy Tonnizam Marto, Aminaton TA Engineering (General). Civil engineering (General) Blasting is an inseparable part of the rock fragmentation process in hard rock mining. As an adverse and undesirable effect of blasting on surrounding areas, airblast-overpressure (AOp) is constantly considered by blast designers. AOp may impact the human and structures in adjacent to blasting area. Consequently, many attempts have been made to establish empirical correlations to predict and subsequently control the AOp. However, current correlations only investigate a few influential parameters, whereas there are many parameters in producing AOp. As a powerful function approximations, artificial neural networks (ANNs) can be utilized to simulate AOp. This paper presents a new approach based on hybrid ANN and particle swarm optimization (PSO) algorithm to predict AOp in quarry blasting. For this purpose, AOp and influential parameters were recorded from 62 blast operations in four granite quarry sites in Malaysia. Several models were trained and tested using collected data to determine the optimum model in which each model involved nine inputs, including the most influential parameters on AOp. In addition, two series of site factors were obtained using the power regression analyses. Findings show that presented PSO-based ANN model performs well in predicting the AOp. Hence, to compare the prediction performance of the PSO-based ANN model, the AOp was predicted using the current and proposed formulas. The training correlation coefficient equals to 0.94 suggests that the PSO-based ANN model outperforms the other predictive models. 2014 Article PeerReviewed Hajihassani, Mohsen and Jahed Armaghani, Danial and Sohaei, Houman and Mohamad, Edy Tonnizam and Marto, Aminaton (2014) Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization. Applied Acoustics, 80 . pp. 57-67. ISSN 0003-682X http://dx.doi.org/10.1016/j.apacoust.2014.01.005 |
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TA Engineering (General). Civil engineering (General) Hajihassani, Mohsen Jahed Armaghani, Danial Sohaei, Houman Mohamad, Edy Tonnizam Marto, Aminaton Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization |
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Blasting is an inseparable part of the rock fragmentation process in hard rock mining. As an adverse and undesirable effect of blasting on surrounding areas, airblast-overpressure (AOp) is constantly considered by blast designers. AOp may impact the human and structures in adjacent to blasting area. Consequently, many attempts have been made to establish empirical correlations to predict and subsequently control the AOp. However, current correlations only investigate a few influential parameters, whereas there are many parameters in producing AOp. As a powerful function approximations, artificial neural networks (ANNs) can be utilized to simulate AOp. This paper presents a new approach based on hybrid ANN and particle swarm optimization (PSO) algorithm to predict AOp in quarry blasting. For this purpose, AOp and influential parameters were recorded from 62 blast operations in four granite quarry sites in Malaysia. Several models were trained and tested using collected data to determine the optimum model in which each model involved nine inputs, including the most influential parameters on AOp. In addition, two series of site factors were obtained using the power regression analyses. Findings show that presented PSO-based ANN model performs well in predicting the AOp. Hence, to compare the prediction performance of the PSO-based ANN model, the AOp was predicted using the current and proposed formulas. The training correlation coefficient equals to 0.94 suggests that the PSO-based ANN model outperforms the other predictive models. |
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Article |
author |
Hajihassani, Mohsen Jahed Armaghani, Danial Sohaei, Houman Mohamad, Edy Tonnizam Marto, Aminaton |
author_facet |
Hajihassani, Mohsen Jahed Armaghani, Danial Sohaei, Houman Mohamad, Edy Tonnizam Marto, Aminaton |
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Hajihassani, Mohsen |
title |
Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization |
title_short |
Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization |
title_full |
Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization |
title_fullStr |
Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization |
title_full_unstemmed |
Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization |
title_sort |
prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization |
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2014 |
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http://eprints.utm.my/id/eprint/54558/ http://dx.doi.org/10.1016/j.apacoust.2014.01.005 |
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13.18916 |