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• W4312069118 abstract "As an increasing number of building components are modularized and prefabricated, tower cranes are required to lift larger and heavier loads. This situation increased the risk of crane failure and the severity of crane accidents, challenging the effectiveness of conventional reactive safety management approaches. Thus, early hazard recognition and proactive control (Prevention through Design, PtD) are necessitated to manage Tower Crane Operation (TCO) hazards. This study proposes an automatic PtD method to manage the Hazard Exposure (HE) brought by one or multiple tower crane operations on construction sites in the construction planning stage by leveraging a path-finding algorithm and Building Information Modelling (BIM)-enabled spatial–temporal analyses. The dynamic distribution of TCO hazards is estimated based on safe and practical lift paths generated by a path-finding algorithm, and visualized as HE heatmaps in BIM models. Three application scenarios of the HE estimation and visualization are explored, including managing critical lifts, improving daily hazard communication on site, and contextualizing scheduling/planning tasks with hazard information. Estimating and visualizing HE in these scenarios are expected to allow planners and safety managers arrange activities and workspaces to minimize the risks of TCO hazards. The contributions of the paper include (1) proposing a generic energy-based quantification model for different types of TCO hazards, (2) increasing the spatial–temporal granularity of the HE estimation and visualization, and (3) promoting proactive controlling measures over the TCO hazards through planning, scheduling, and the coordination of crane operations with other on-site activities." @default.
• W4312069118 created "2023-01-04" @default.
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• W4312069118 date "2023-04-01" @default.
• W4312069118 modified "2023-09-29" @default.
• W4312069118 title "Estimating and visualizing the exposure to tower crane operation hazards on construction sites" @default.
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• W4312069118 doi "https://doi.org/10.1016/j.ssci.2022.106044" @default.
• W4312069118 hasPublicationYear "2023" @default.
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