An Association Rule Mining Model For The Assessment Of The Correlations Between The Attributes Of Severe Accidents

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dc.WoS.categoriesEngineering, Civilen_US
dc.authorid0000-0002-1000-1678en_US
dc.contributor.authorAyhan, Bilal Umut
dc.contributor.authorDoğan, Neşet Berkay
dc.contributor.authorTokdemir, Onur Behzat
dc.date.accessioned2020-12-21T11:48:12Z
dc.date.available2020-12-21T11:48:12Z
dc.date.issued2020
dc.description.abstractIdentifying the correlations between the attributes of severe accidents could be vital to preventing them. If such relationships were known dynamically, it would be possible to take preventative actions against accidents. The paper aims to develop an analytical model that is adaptable for each type of data to create preventative measures that will be suitable for any computational systems. The present model collectively shows the relationships between the attributes in a coherent manner to avoid severe accidents. In this respect, Association Rule Mining (ARM) is used as the technique to identify the correlations between the attributes. The research adopts a positivist approach to adhere to the factual knowledge concerning nine different accident types through case studies and quantitative measurements in an objective nature. ARM was exemplified with nine different types of construction accidents to validate the adaptability of the proposed model. The results show that each accident type has different characteristics with varying combinations of the attribute, and analytical model accomplished to accommodate variation through the dataset. Ultimately, professionals can identify the cause-effect relationships effectively and set up preventative measures to break the link between the accident causing factors.en_US
dc.fullTextLevelFull Texten_US
dc.identifier.doi10.3846/jcem.2020.12316en_US
dc.identifier.issn1822-3605
dc.identifier.issn1392-3730
dc.identifier.scopus2-s2.0-85083653049en_US
dc.identifier.urihttps://hdl.handle.net/11411/2931
dc.identifier.urihttps://doi.org/10.3846/jcem.2020.12316
dc.identifier.wosWOS:000530873900001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.issue4en_US
dc.language.isoenen_US
dc.nationalInternationalen_US
dc.numberofauthors3en_US
dc.pages315-330en_US
dc.publisherVilnius Gediminas Tech Univ.en_US
dc.relation.ispartofJournal Of Civil Engineering And Managementen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAccident analysisen_US
dc.subjectAssociation Rule Miningen_US
dc.subjectData miningen_US
dc.subjectNetwork analysisen_US
dc.titleAn Association Rule Mining Model For The Assessment Of The Correlations Between The Attributes Of Severe Accidentsen_US
dc.typeArticleen_US
dc.volume26en_US

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