Show simple item record

dc.contributor.authorJang, Hyong Doo
dc.contributor.authorTopal, Erkan
dc.date.accessioned2020-08-15T09:18:45Z
dc.date.available2020-08-15T09:18:45Z
dc.date.issued2013
dc.identifier.citationJang, H.D. and Topal, E. 2013. Underground Blasting Optimization by Artificial Intelligence Techniques. In: The 3rd International Workshop on Soft Computing and Disaster Control, 9th Nov 2013, Stikom Bali, Indonesia.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/80583
dc.description.abstract

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
dc.date.updated2020-08-15T09:18:44Z
curtin.departmentWASM: Minerals, Energy and Chemical Engineering
curtin.accessStatusIn process
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]


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record