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dc.contributor.authorLiu, Xin
dc.contributor.authorSong, Y.
dc.contributor.authorYi, W.
dc.contributor.authorWang, Xiangyu
dc.contributor.authorZhu, J.
dc.date.accessioned2018-05-18T07:59:59Z
dc.date.available2018-05-18T07:59:59Z
dc.date.created2018-05-18T00:22:58Z
dc.date.issued2018
dc.identifier.citationLiu, 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).
dc.identifier.urihttp://hdl.handle.net/20.500.11937/67848
dc.identifier.doi10.1061/(ASCE)CO.1943-7862.0001495
dc.description.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.

dc.publisherAmerican Society of Civil Engineers
dc.titleComparing the Random Forest with the Generalized Additive Model to Evaluate the Impacts of Outdoor Ambient Environmental Factors on Scaffolding Construction Productivity
dc.typeJournal Article
dcterms.source.volume144
dcterms.source.number6
dcterms.source.issn0733-9364
dcterms.source.titleJournal of Construction Engineering and Management
curtin.departmentSustainability Policy Institute
curtin.accessStatusFulltext not available


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