Decision tree analysis of construction fall accidents involving roofers

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dc.contributor.authorMistikoglu, Gulgun
dc.contributor.authorGerek, Ibrahim Halil
dc.contributor.authorErdis, Ercan
dc.contributor.authorUsmen, P. E. Mumtaz
dc.contributor.authorCakan, Hulya
dc.contributor.authorKazan, Emrah Esref
dc.date.accessioned2024-09-18T20:19:59Z
dc.date.available2024-09-18T20:19:59Z
dc.date.issued2015
dc.departmentHatay Mustafa Kemal Üniversitesien_US
dc.description.abstractData mining (DM) techniques have not been adopted on a wide scale for construction accident data analysis. The decision tree (DT) technique is a supervised data mining method that shows good promise for this purpose. The C5.0 and CHAID algorithms were employed in this study to construct decision trees and to extract rules that show the associations between the input and output variables (attributes) for roofer fall accidents. Data obtained from the US Occupational Safety and Health Administration (OSHA) was incorporated in this research. Degree of injury (fatality vs. nonfatal injury) was selected as the output attribute, and a multitude of input attributes were included in the study. Two models based on the algorithms were developed and validated. The results showed that decision trees provided specific and detailed depictions of the associations between the attributes. It was found that fatality chances increased with increasing fall distance and decreased when safety training was provided. The most important input attributes in the models were identified as the fall distance, fatality/injury cause, safety training, and construction operation prompting fall, meaning that these factors had the best predictive power related to whether a roofer fall accident would result in a fatality or nonfatal injury. (C) 2014 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.eswa.2014.10.009
dc.identifier.endpage2263en_US
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-84910644907en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage2256en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2014.10.009
dc.identifier.urihttps://hdl.handle.net/20.500.12483/9995
dc.identifier.volume42en_US
dc.identifier.wosWOS:000347579500043en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems With Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFall accidentsen_US
dc.subjectData miningen_US
dc.subjectDegree of injuryen_US
dc.subjectDecision treeen_US
dc.subjectPredictive poweren_US
dc.titleDecision tree analysis of construction fall accidents involving roofersen_US
dc.typeArticleen_US

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