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• W4221096280 abstract "Poor hazard recognition is a significant safety issue in the construction industry. Past research efforts have focused on assessing, developing, and improving the hazard recognition skill of construction workers. However, much of this useful information is fragmented and dispersed across the broader literature. The current article focuses on summarizing the state of the science and identifying future opportunities to combat poor hazard recognition in the construction industry. This study adopted a three-level “smart literature review” approach unlike traditional literature reviews. To locate relevant research, keyword searches that included “construction hazard recognition” or “construction hazard identification” were performed in the Scopus database. After a two-round screening process, 168 relevant documents were selected for further analysis. Finally, this study leveraged a machine learning algorithm, Latent Dirichlet Allocation (LDA), to perform the content analysis. The analysis resulted in eight separate topics that have been discussed in the broader construction hazard recognition literature. The research findings will serve as a resource for researchers and construction leaders to better understand the state of the science on construction hazard recognition and identify potential directions for future research opportunities." @default.
• W4221096280 created "2022-04-03" @default.
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• W4221096280 date "2022-03-07" @default.
• W4221096280 modified "2023-09-27" @default.
• W4221096280 title "Construction Hazard Recognition: A Smart Literature Review" @default.
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• W4221096280 doi "https://doi.org/10.1061/9780784483985.042" @default.
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