Project characteristics-based predicting the likelihood of occupational accidents in public school maintenances using a topological approach

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dc.authoridhttps://orcid.org/0000-0002-8616-799X
dc.contributor.authorYiğit, Uğur
dc.contributor.authorKazar, Gökhan
dc.date.accessioned2025-05-26T08:06:10Z
dc.date.available2025-05-26T08:06:10Z
dc.date.issued2025
dc.departmentMühendislik ve Mimarlık Fakültesi
dc.description.abstractOccupational accidents are common in construction projects. Although several previous studies have focused on this complex issue from different perspectives, such as predicting accidents for the general construction process, few studies have focused on the impact of project characteristics on the likelihood of accidents in building maintenance projects. Artificial intelligence-based predictive models for workplace accidents typically use readymade algorithms or traditional methods like trial and error. Therefore, the main objective of this study is to predict the likelihood of occupational accidents in building maintenance projects by following a new feature selection process based on the topological approach. The information on the 1807 public school maintenance project was included in this study to test the proposed mathematical approach. Commonly used 7 different machine learning algorithms and a combination of these algorithms called a hybrid model was selected to apply the topological approach to the feature selection process. The results show that 5 out of 7 algorithms such as Stochastic Gradient Boosting (SGB), Extreme Gradient Boosting (EGB), Linear Discriminant Analysis (LDA), Gaussian Naive Bayes (GNB), and Hybrid (HYB) models show better performance after applying the topological technique. The main predictors of the likelihood of workplace accidents in these algorithms are site delivery (T2), cost breakdown ratio (F1), total duration (T1), and contractor size (P1). Using this approach, construction professionals can develop and implement more effective AI-based proactive safety management systems for maintenance projects.
dc.identifier.doi10.1016/j.ssci.2024.106764
dc.identifier.endpage12
dc.identifier.issn0925-7535
dc.identifier.issn1879-1042
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/11363/9814
dc.identifier.volume184
dc.identifier.wos001403508600001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.institutionauthorKazar, Gökhan
dc.institutionauthoridhttps://orcid.org/0000-0002-8616-799X
dc.language.isoen
dc.publisherELSEVIER, RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
dc.relation.ispartofSAFETY SCIENCE
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectRough topology
dc.subjectFeature selection
dc.subjectOccupational accidents
dc.subjectMachine learning
dc.subjectMaintenance project
dc.subjectSchool buildings
dc.subjectProject characteristics
dc.titleProject characteristics-based predicting the likelihood of occupational accidents in public school maintenances using a topological approach
dc.typeArticle

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