The principal aim of present study was the application of the Artificial Neural Network (ANN) system to the determination of the optimal powder factors, based on a series of observations and numerical experiments. The input parameters were 12 geological conditions. The data for the NN application in this study were collected in the spillway tunnel of Kosar Dam, which located in West south of Iran. An optimum NN model was determined by training and testing models with the collected data. It was shown that the NN model could predict the powder factor depending upon the selected input parameters. In addition, the strength of the relationship between the powder factor and the 14 input parameters was evaluated by three different sensitivity analysis methods, i.e. the analysis of relative strength effect, the cosine amplitude method and the fractional factorial design. From these analyses, not only the dominant factors among the input parameters, but also their interactions could be determined.
Saemi, M., & Gilani, S. (2006). Determining of Optimal Powder Factor in Tunnel Blasting Using Neural Network Systems. Journal of Mining Engineering, 1(1), 49-55.
MLA
M Saemi; S.O Gilani. "Determining of Optimal Powder Factor in Tunnel Blasting Using Neural Network Systems". Journal of Mining Engineering, 1, 1, 2006, 49-55.
HARVARD
Saemi, M., Gilani, S. (2006). 'Determining of Optimal Powder Factor in Tunnel Blasting Using Neural Network Systems', Journal of Mining Engineering, 1(1), pp. 49-55.
VANCOUVER
Saemi, M., Gilani, S. Determining of Optimal Powder Factor in Tunnel Blasting Using Neural Network Systems. Journal of Mining Engineering, 2006; 1(1): 49-55.
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