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    Comparing the Random Forest with the Generalized Additive Model to Evaluate the Impacts of Outdoor Ambient Environmental Factors on Scaffolding Construction Productivity

    Access Status
    Fulltext not available
    Authors
    Liu, Xin
    Song, Y.
    Yi, W.
    Wang, Xiangyu
    Zhu, J.
    Date
    2018
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Liu, X. and Song, Y. and Yi, W. and Wang, X. and Zhu, J. 2018. Comparing the Random Forest with the Generalized Additive Model to Evaluate the Impacts of Outdoor Ambient Environmental Factors on Scaffolding Construction Productivity. Journal of Construction Engineering and Management. 144 (6).
    Source Title
    Journal of Construction Engineering and Management
    DOI
    10.1061/(ASCE)CO.1943-7862.0001495
    ISSN
    0733-9364
    School
    Sustainability Policy Institute
    Funding and Sponsorship
    http://purl.org/au-research/grants/arc/LP140100873
    URI
    http://hdl.handle.net/20.500.11937/67848
    Collection
    • Curtin Research Publications
    Abstract

    © 2018 American Society of Civil Engineers. The improvement of construction productivity has always been a key concern for both researchers and project managers. Several studies have analyzed construction productivity from different perspectives; however, little research has been conducted to evaluate the impact of outdoor ambient environmental factors on construction productivity, especially at the project level. Therefore, to assess such impacts, a nonparametric regression model - the generalized additive model (GAM) - and a nonlinear machine learning model - random forest (RF) - are comparatively used to assess these contributors on the scaffolding construction performance factor (PF). The meteorological variables used in this study include temperature, humidity, ambient pressure, wind speed and wind direction, specific weather event (clear day, fog, rain, or thunderstorm), and the ultraviolet (UV) index. Results demonstrate that the joint meteorological factors play a key role in construction PF variation, with contribution ranging from 32.50% (GAM) to 59.41% (RF). The better performance of RF and GAM shows that the relationship between outdoor ambient environment and construction productivity is nonlinear and should be built by nonlinear models.

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