Development of a novel flyrock distance prediction model using BPNN for providing blasting operation safety. Issue 3 (April 2016)

• Record Type: Journal Article
• Authors: Yari, M.; Bagherpour, R.; Jamali, S.; Shamsi, R.
• Is Part Of: Neural computing & applications. Volume 27:Issue 3(2016)
• Page Start: 699
• Page End: 706
• Publication Date: 2016-04
• Subjects: Flyrock -- Back-propagation neural network -- Safety -- Sungun copper mine; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-1889-9
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract One of the threatening safety problems in mines is flyrock distance range through blasting operation. Inaccurate evaluation of flyrock can cause fatal and nonfatal accidents. The presented results in this paper verify efficiency of artificial neural network in prediction of flyrock considering all influencing parameters such as: hole diameter, height, subdrilling, number of holes, spacing, burden, ANFO amount, dynamite weight, stemming, powder factor, specific drilling, and delay time. In this research, optimum structure of network was determined by studying different transfer functions and number of the neurons using a programming code. In this case, optimum structure configuration is logsig transfer functions for the two hidden layers and tansig or logsig one for output, and there are eight neurons in each hidden layers. By calculating strength of relationship between flyrock and all influencing parameters using cosine amplitude method (CAM), the powder factor is defined as most effective parameter on the flyrock.