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Application of artificial neural networks to predict blast-induced ground vibration in a diamond mine

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dc.contributor.author Gaopale, Kesalopa
dc.contributor.author Jamisola, Rodrigo S.
dc.contributor.author Seitshiro, Itumeleng
dc.date.accessioned 2020-08-19T08:26:29Z
dc.date.available 2020-08-19T08:26:29Z
dc.date.issued 2019-06
dc.identifier.citation Gaopale, K., Jamisola, R. S. and Seitshiro, I. (2019) Application of artificial neural networks to predict blast-induced ground vibration in a diamond mine. In Jamisola, Rodrigo S. Jr (ed.) BIUST Research and Innovation Symposium 2019 (RDAIS 2019); 1(1), 88- 93. en_US
dc.identifier.issn 2521-2292
dc.identifier.uri http://repository.biust.ac.bw/handle/123456789/178
dc.description.abstract In this paper, ground vibration that is induced by blasting was predicted using data gathered from a diamond mine. Artificial neural network (ANN) is used to train the model from the 94 blast dataset using Levenberg–Marquardt algorithm, and we tested and verified the built model. Different ANN models were compared using Root mean square error (RMSE) and coefficient of determinant (R2), and the optimum ANN model was selected. Blasthole depth, blasthole diameter, burden, spacing, stemming length, powder factor, maximum charge and distance from the blast face to the monitoring point were used as input parameters. Ground vibration was the output parameter that was predicted using the built model. Processes in building the machine learning model are presented, together with prediction results and are compared against each other. 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 Artificial neural network en_US
dc.subject Blasting en_US
dc.subject Ground vibration en_US
dc.title Application of artificial neural networks to predict blast-induced ground vibration in a diamond mine en_US
dc.description.level phd en_US
dc.description.accessibility unrestricted en_US
dc.description.department mge en_US


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