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dc.contributor.authorJang, Hyong Doo
dc.contributor.authorTopal, Erkan
dc.identifier.citationJang, H.D. and Topal, E. 2013. Underground Blasting Optimization by Artificial Intelligence Techniques, in Proceedings of the 3rd International Workshop on Soft Computing and Disaster Control (SocDic 2013) Workshop, Nov 9-10 2013. Bali, Indonesia.

Drilling and blasting is recognized as the most economical method in hard rock mining and it has been widely applied both surface and underground mining. Over the past few decades, the efficiency of mine blasting has been greatly increased but still there are unavoidable drawbacks of drilling and blasting method. Blasting hazards such as ground vibration, flyrock, air blast and toxic fumes should be considered before blasting design stage. Especially blasting in tunnelling and underground mine, uneven break of perimeter area is an essential issue not only for ensuring the safe working environment but also for the profitability of the project. In this paper, some of artificial intelligence applications to predict blasting hazards are reviewed. Then, nonlinear multiple regression analysis and artificial neuron network (ANN) models were developed and applied to predict uneven break on a tunnel project located in Gumi, Korea. The results indicated that ANN was successfully utilized to predict uneven break of tunnel blasting.

dc.subject0914 - Resources Engineering and Extractive Metallurgy
dc.titleUnderground Blasting Optimization by Artificial Intelligence Techniques
dc.typeConference Paper
dcterms.source.conferenceThe 3rd International Workshop on Soft Computing and Disaster Control
dcterms.source.conference-start-date9 Nov 2013
dcterms.source.conferencelocationStikom Bali, Indonesia
curtin.departmentWASM: Minerals, Energy and Chemical Engineering
curtin.accessStatusFulltext not available
curtin.facultyFaculty of Science and Engineering
curtin.contributor.orcidJang, Hyong Doo [0000-0002-3978-5840]
dcterms.source.conference-end-date10 Nov 2013
curtin.contributor.scopusauthoridJang, Hyong Doo [55797412200]

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