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Bulletin of Engineering Geology and the Environment

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01-08-2015 | Original Article

Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm

Authors: Mohsen Hajihassani, Danial Jahed Armaghani, Aminaton Marto, Edy Tonnizam Mohamad

Published in: Bulletin of Engineering Geology and the Environment | Issue 3/2015

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Abstract

This paper presents a new hybrid artificial neural network (ANN) optimized by imperialist competitive algorithm (ICA) to predict peak particle velocity (PPV) resulting from quarry blasting. For this purpose, 95 blasting works were precisely monitored in a granite quarry site in Malaysia and PPV values were accurately recorded in each operation. Furthermore, the most influential parameters on PPV were measured and used to train the ICA-ANN model. Considering the measured data from the granite quarry site, a new empirical equation was developed to predict PPV. For comparison, a pre-developed ANN model was developed for PPV prediction. The results demonstrated that the proposed ICA-ANN model is able to predict blasting-induced PPV better than other presented techniques.

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Title: Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm
Authors: Mohsen Hajihassani
Danial Jahed Armaghani
Aminaton Marto
Edy Tonnizam Mohamad
Publication date: 01-08-2015
Publisher: Springer Berlin Heidelberg
Published in: Bulletin of Engineering Geology and the Environment / Issue 3/2015
Print ISSN: 1435-9529
Electronic ISSN: 1435-9537
DOI: https://doi.org/10.1007/s10064-014-0657-x

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