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Airblast prediction in a blasting operation using artificial intelligence

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dc.contributor.author Gaopale, Kesalopa
dc.contributor.author Jamisola, Rodrigo S.
dc.contributor.author Seitshiro, Itumeleng
dc.date.accessioned 2020-08-18T14:51:14Z
dc.date.available 2020-08-18T14:51:14Z
dc.date.issued 2019-06
dc.identifier.citation Gaopale, K., Jamisola, R. S. and Seitshiro, I. (2019) Airblast prediction in a blasting operation using artificial intelligence. In Jamisola, Rodrigo S. Jr (ed.) BIUST Research and Innovation Symposium 2019 (RDAIS 2019); 1(1), 82- 87. en_US
dc.identifier.issn 2521-2292
dc.identifier.uri http://repository.biust.ac.bw/handle/123456789/174
dc.description.abstract This paper presents the use of artificial neural network (ANN) to predict airblast that is induced by blasting in a diamond mine. A total of 94 blasting datasets were used to develop and train the ANN models using Levenberg–Marquardt algorithm. The input parameters were: burden, spacing, blasthole depth, blasthole diameter, stemming length, distance from the blast face, powder factor, and maximum charge. Airblast was the output parameter. Its values were predicted after the model was built. The ANN model with 8-12-1 architecture proved to have a better performance when compared to other ANN models. Comparisons were based on coefficient of determinant (R2) and root mean square error (RMSE). The processes of building and characterization of the machine learning model are shown together with results on prediction accuracy. Each result is compared against different ANN architecture, transfer functions, and number of hidden neurons. en_US
dc.description.sponsorship Botswana International University of Science and Technology en_US
dc.language.iso en en_US
dc.publisher Botswana International University of Science and Technology (BIUST) en_US
dc.subject Airblast en_US
dc.subject Artificial neural network en_US
dc.subject Blasting en_US
dc.title Airblast prediction in a blasting operation using artificial intelligence en_US
dc.description.level phd en_US
dc.description.accessibility unrestricted en_US
dc.description.department mge en_US


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