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Blast-induced air and ground vibration prediction: a particle swarm optimization-based artificial neural network approach

Hajihassani, Mohsen and Armaghani, Danial Jahed and Monjezi, Masoud and Mohamad, Edy Tonnizam and Marto, Aminaton (2015) Blast-induced air and ground vibration prediction: a particle swarm optimization-based artificial neural network approach. Environmental Earth Sciences, 74 (4). pp. 2799-2817. ISSN 1866-6280

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Official URL: http://dx.doi.org/10.1007/s12665-015-4274-1

Abstract

Mines, quarries, and construction sites face environmental damages due to blasting environmental impacts such as ground vibration and air overpressure. These phenomena may cause damage to structures, groundwater, and ecology of the nearby area. Several empirical predictors have been proposed by various scholars to estimate ground vibration and air overpressure, but these methods are inapplicable in many conditions. However, prediction of ground vibration and air overpressure is complicated as a consequence of the fact that a large number of influential parameters are involved. In this study, a hybrid model of an artificial neural network and a particle swarm optimization algorithm was implemented to predict ground vibration and air overpressure induced by blasting. To develop this model, 88 datasets including the parameters with the greatest influence on ground vibration and air overpressure were collected from a granite quarry site in Malaysia. The results obtained by the proposed model were compared with the measured values as well as with the results of empirical predictors. The results indicate that the proposed model is an applicable and accurate tool to predict ground vibration and air overpressure induced by blasting.

Item Type:Article
Uncontrolled Keywords:air overpressure, artificial neural network, ground vibration, particle swarm optimization, vibration blasting impacts
Subjects:A General Works
ID Code:57971
Deposited By: Haliza Zainal
Deposited On:04 Dec 2016 12:07
Last Modified:09 Apr 2017 15:30

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