Prediction of blast-induced air overpressure: a hybrid AI-based predictive model
Blast operations in the vicinity of residential areas usually produce significant environmental problems which may cause severe damage to the nearby areas. Blast-induced air overpressure (AOp) is one of the most important environmental impacts of blast operations which needs to be predicted to minim...
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my.utm.549862017-07-31T08:24:49Z http://eprints.utm.my/id/eprint/54986/ Prediction of blast-induced air overpressure: a hybrid AI-based predictive model Armaghani, Danial Jahed Hajihassani, Mohsen Marto, Aminaton Faradonbeh, Roohollah Shirani Mohamad, Edy Tonnizam TA Engineering (General). Civil engineering (General) Blast operations in the vicinity of residential areas usually produce significant environmental problems which may cause severe damage to the nearby areas. Blast-induced air overpressure (AOp) is one of the most important environmental impacts of blast operations which needs to be predicted to minimize the potential risk of damage. This paper presents an artificial neural network (ANN) optimized by the imperialist competitive algorithm (ICA) for the prediction of AOp induced by quarry blasting. For this purpose, 95 blasting operations were precisely monitored in a granite quarry site in Malaysia and AOp values were recorded in each operation. Furthermore, the most influential parameters on AOp, including the maximum charge per delay and the distance between the blast-face and monitoring point, were measured and used to train the ICA-ANN model. Based on the generalized predictor equation and considering the measured data from the granite quarry site, a new empirical equation was developed to predict AOp. For comparison purposes, conventional ANN models were developed and compared with the ICA-ANN results. The results demonstrated that the proposed ICA-ANN model is able to predict blast-induced AOp more accurately than other presented techniques. Penerbit UTM 2015-11-01 Article PeerReviewed Armaghani, Danial Jahed and Hajihassani, Mohsen and Marto, Aminaton and Faradonbeh, Roohollah Shirani and Mohamad, Edy Tonnizam (2015) Prediction of blast-induced air overpressure: a hybrid AI-based predictive model. Environmental Monitoring and Assessment, 187 (11). p. 666. ISSN 0167-6369 http://dx.doi.org/10.1007/s10661-015-4895-6 DOI:10.1007/s10661-015-4895-6 |
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TA Engineering (General). Civil engineering (General) Armaghani, Danial Jahed Hajihassani, Mohsen Marto, Aminaton Faradonbeh, Roohollah Shirani Mohamad, Edy Tonnizam Prediction of blast-induced air overpressure: a hybrid AI-based predictive model |
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Blast operations in the vicinity of residential areas usually produce significant environmental problems which may cause severe damage to the nearby areas. Blast-induced air overpressure (AOp) is one of the most important environmental impacts of blast operations which needs to be predicted to minimize the potential risk of damage. This paper presents an artificial neural network (ANN) optimized by the imperialist competitive algorithm (ICA) for the prediction of AOp induced by quarry blasting. For this purpose, 95 blasting operations were precisely monitored in a granite quarry site in Malaysia and AOp values were recorded in each operation. Furthermore, the most influential parameters on AOp, including the maximum charge per delay and the distance between the blast-face and monitoring point, were measured and used to train the ICA-ANN model. Based on the generalized predictor equation and considering the measured data from the granite quarry site, a new empirical equation was developed to predict AOp. For comparison purposes, conventional ANN models were developed and compared with the ICA-ANN results. The results demonstrated that the proposed ICA-ANN model is able to predict blast-induced AOp more accurately than other presented techniques. |
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Article |
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Armaghani, Danial Jahed Hajihassani, Mohsen Marto, Aminaton Faradonbeh, Roohollah Shirani Mohamad, Edy Tonnizam |
author_facet |
Armaghani, Danial Jahed Hajihassani, Mohsen Marto, Aminaton Faradonbeh, Roohollah Shirani Mohamad, Edy Tonnizam |
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Armaghani, Danial Jahed |
title |
Prediction of blast-induced air overpressure: a hybrid AI-based predictive model |
title_short |
Prediction of blast-induced air overpressure: a hybrid AI-based predictive model |
title_full |
Prediction of blast-induced air overpressure: a hybrid AI-based predictive model |
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Prediction of blast-induced air overpressure: a hybrid AI-based predictive model |
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Prediction of blast-induced air overpressure: a hybrid AI-based predictive model |
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prediction of blast-induced air overpressure: a hybrid ai-based predictive model |
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Penerbit UTM |
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2015 |
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http://eprints.utm.my/id/eprint/54986/ http://dx.doi.org/10.1007/s10661-015-4895-6 |
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