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    A framework for establishing early warning system for working in hot environments

    Access Status
    Fulltext not available
    Authors
    Yi, W.
    Zhu, J.
    Liu, Xin
    Wang, X.
    Chan, A.
    Date
    2016
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Yi, W. and Zhu, J. and Liu, X. and Wang, X. and Chan, A. 2016. A framework for establishing early warning system for working in hot environments, in Proceedings of the 33rd International Symposium for Automation and Robotics in Construction, 18-21 Jul 2016, pp. 1025-1031. Auburn, Alabama: International Association for Automation and Robotics in Construction.
    Source Title
    ISARC 2016 - 33rd International Symposium on Automation and Robotics in Construction
    Additional URLs
    http://www.iaarc.org/publications/fulltext/ISARC2016-Paper213.pdf
    School
    Department of Construction Management
    URI
    http://hdl.handle.net/20.500.11937/12108
    Collection
    • Curtin Research Publications
    Abstract

    Heat stress is a well-known occupational hazard, and climate change together with the increased frequency and intensity of extreme heat events has made risks more severe and widespread. The liquefied natural gas (LNG) construction industry is found to be more susceptible to heat stress than other industries because LNG workers are subjected to heat stress not only from outdoor strenuous physical work but also in confined working spaces which could be even worse. Traditional management of heat stress are by and large some "dos and don'ts" and are not based on scientific measurements. The continuing high frequency of heat related incidents in the construction LNG industry calls for better approaches to detect impending attacks of heat stress. The purpose of study is to develop a smart work site against hot climates using modern technologies. The proposed system could be automated by information communication technology, sensor technology, Radio Frequency Identification (RFID) technology, to protect the wellbeing for those who have to work in hot environments. A Bayesian networks model could be established to evaluate the heat strain of workers. 3D environment map of a LNG site could be simulated by the readings of environmental sensors and trilinear interpolation. The work duration, location, and workplace heat exposure of workers could be monitored during the work and warning alerts would be sent to the workers when certain threshold exceeds. The proposed smart work site for early warning of heat stress would be of tremendous value in better safeguarding workers' health and safety by reducing the occurrences of heat stress on site.

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