Journal
BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
Volume 74, Issue 3, Pages 873-886Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s10064-014-0657-x
Keywords
Blast safety area; Ground vibration; Peak particle velocity; Artificial neural network; Imperialist competitive algorithm
Funding
- Ministry of Science, Technology, and Innovation, Malaysia [03-01-06-SF1185 (UTM 4S072)]
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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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