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Estimating the continuous risk of accidents occurring in the mining industry in South Africa

dc.contributor.authorVan den Honert, Andrew Francisen_ZA
dc.contributor.authorVlok, Pieter-Janen_ZA
dc.date.accessioned2016-09-05T08:00:16Z
dc.date.available2016-09-05T08:00:16Z
dc.date.issued2015
dc.identifier.citationVan Den Honert, A. F. & Vlok, P. J. 2015. Estimating the continuous risk of accidents occurring in the mining industry in South Africa. South African Journal of Industrial Engineering, 26(3):71-85, doi:10.7166/26-3-1121
dc.identifier.issn2224-7890 (online)
dc.identifier.issn1012-277X (print)
dc.identifier.otherdoi:10.7166/26-3-1121
dc.identifier.urihttp://hdl.handle.net/10019.1/99556
dc.descriptionCITATION: Van Den Honert, A. F. & Vlok, P. J. 2015. Estimating the continuous risk of accidents occurring in the mining industry in South Africa. South African Journal of Industrial Engineering, 26(3):71-85, doi:10.7166/26-3-1121.
dc.descriptionThe original publication is available at http://sajie.journals.ac.za
dc.description.abstractThis study contributes to the on-going efforts to improve occupational safety in the mining industry by creating a model capable of predicting the continuous risk of occupational accidents occurring. Contributing factors were identified and their sensitivity quantified. The approach included using an Artificial Neural Network (ANN) to identify patterns between the input attributes and to predict the continuous risk of accidents occurring. The predictive Artificial Neural Network (ANN) model used in this research was created, trained, and validated in the form of a case study with data from a platinum mine near Rustenburg in South Africa. This resulted in meaningful correlation between the predicted continuous risk and actual accidents.en_ZA
dc.description.abstractHierdie studie probeer ’n bydrae lewer om beroepsveiligheid in die mynbedryf te verbeter deur ’n model te skep wat in staat is daartoe om die voortdurende risiko’s van moontlike werksongelukke te voorspel. Bydraende faktore is geïdentifiseer en hulle sensitiwiteit is gekwantifiseer. Die benadering sluit in die gebruik van ’n Kunsmatige Neurale Netwerk (ANN) wat patrone identifiseer tussen die bydraende kenmerke en om die aanhoudende risiko van ongelukke te voorspel. Hierdie model was geskep, opgelei en gevalideer tydens ’n gevallestudie waar die data verkry is van ’n platinum-myn naby Rustenburg in Suid-Afrika. Die gevolgtrekking was dat ’n betekenisvolle korrelasie tussen die voorspelbare voortdurende risiko’s en werklike ongelukke bestaan.af_ZA
dc.description.urihttp://sajie.journals.ac.za/pub/article/view/1121
dc.format.extent15 pages
dc.language.isoen_ZAen_ZA
dc.publisherSAIIE
dc.subjectSafetyen_ZA
dc.subjectMining -- Safetyen_ZA
dc.subjectAccidents -- Prevention -- Miningen_ZA
dc.subjectMining accidentsen_ZA
dc.titleEstimating the continuous risk of accidents occurring in the mining industry in South Africaen_ZA
dc.typeArticleen_ZA
dc.description.versionPublisher's version
dc.rights.holderAuthors retain copyright


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