Construction accident prevention: A systematic review of machine learning approaches.
BACKGROUND: The construction industry is an important productive sector worldwide. However, the industry is also responsible for high numbers of work-related accidents, which highlights the necessity for improving safety management on construction sites. In parallel, technological applications such...
| Publicado en: | Work Vol. 76; no. 2; pp. 507 - 520 |
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| Autores principales: | , , |
| Formato: | pictorial research systematic review tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=173163330&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 173163330 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10519815 3RC jtl: Work issn: 10519815 maglogo: N pubinfo: dt: 2023 vid: 76 iid: 2 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 173163330 162410039 173163330 173163330 10.3233/WOR-220533 173163330 ppf: 507 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Construction accident prevention: A systematic review of machine learning approaches. aug: au: Cavalcanti, Marília Lessa, Luciano Vasconcelos, Bianca M. affil: Polytechnique School of Pernambuco (POLI), University of Pernambuco (UPE), Recife, Pernambuco, Brazil sug: subj: Construction Industry Occupational-Related Injuries Prevention and Control Machine Learning Occupational Safety Technology, Medical Human Systematic Review Bibliometrics World Wide Web Databases, Health Descriptive Statistics Deep Learning ab: BACKGROUND: The construction industry is an important productive sector worldwide. However, the industry is also responsible for high numbers of work-related accidents, which highlights the necessity for improving safety management on construction sites. In parallel, technological applications such as machine learning (ML) are used in many productive sectors, including construction, and have proved significant in process optimizations and decision-making. Thus, advanced studies are required to comprehend the best way of using this technology to enhance construction site safety. OBJECTIVE: This research developed a systematic literature review using ten scientific databases to retrieve relevant publications and fill the knowledge gaps regarding ML applications in construction accident prevention. METHODS: This study examined 73 scientific articles through bibliometric research and descriptive analysis. RESULTS: The results showed the publications timeline and the most recurrent journals, authors, institutions, and countries-regions. In addition, the review discovered information about the developed models, such as the research goals, the ML methods used, and the data features. The research findings revealed that USA and China are the leading countries regarding publications. Also, Support Vector Machine – SVM was the most used ML method. Furthermore, most models used textual data as a source, generally related to inspection reports and accident narratives. The data approach was usually related to facts before an accident (proactive data). CONCLUSION: The review highlighted improvement proposals for future works and provided insights into the application of ML in construction safety management. pubtype: Academic Journal doctype: pictorial research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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