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

• Main Authors: Hassani, Mohsen, Armaghani, Danial Jahed, Marto, Aminaton, Mohamad, Edy Tonnizam
• Format: Article
• Published: Springer 2015
• Subjects: TA Engineering (General). Civil engineering (General)
• Online Access: http://eprints.utm.my/id/eprint/55534/; http://dx.doi.org/10.1007/s10064-014-0657-x
• Institution: Universiti Teknologi Malaysia

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