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.accessioned2025-01-06T17:37:48Z
dc.date.available2025-01-06T17:37:48Z
dc.date.issued2015
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.
dc.identifier.doi10.1016/j.eswa.2014.10.009
dc.identifier.endpage2263
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.issue4
dc.identifier.scopus2-s2.0-84910644907
dc.identifier.scopusqualityQ1
dc.identifier.startpage2256
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2014.10.009
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2374
dc.identifier.volume42
dc.identifier.wosWOS:000347579500043
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofExpert Systems With Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectFall accidents
dc.subjectData mining
dc.subjectDegree of injury
dc.subjectDecision tree
dc.subjectPredictive power
dc.titleDecision tree analysis of construction fall accidents involving roofers
dc.typeArticle

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