Comparing the Random Forest with the Generalized Additive Model to Evaluate the Impacts of Outdoor Ambient Environmental Factors on Scaffolding Construction Productivity
© 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 evalua...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Published: |
American Society of Civil Engineers
2018
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| Online Access: | http://purl.org/au-research/grants/arc/LP140100873 http://hdl.handle.net/20.500.11937/67848 |
| _version_ | 1848761674867146752 |
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| author | Liu, Xin Song, Y. Yi, W. Wang, Xiangyu Zhu, J. |
| author_facet | Liu, Xin Song, Y. Yi, W. Wang, Xiangyu Zhu, J. |
| author_sort | Liu, Xin |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 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. |
| first_indexed | 2025-11-14T10:35:26Z |
| format | Journal Article |
| id | curtin-20.500.11937-67848 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:35:26Z |
| publishDate | 2018 |
| publisher | American Society of Civil Engineers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-678482023-02-02T03:24:11Z Comparing the Random Forest with the Generalized Additive Model to Evaluate the Impacts of Outdoor Ambient Environmental Factors on Scaffolding Construction Productivity Liu, Xin Song, Y. Yi, W. Wang, Xiangyu Zhu, J. © 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. 2018 Journal Article http://hdl.handle.net/20.500.11937/67848 10.1061/(ASCE)CO.1943-7862.0001495 http://purl.org/au-research/grants/arc/LP140100873 American Society of Civil Engineers restricted |
| spellingShingle | Liu, Xin Song, Y. Yi, W. Wang, Xiangyu Zhu, J. Comparing the Random Forest with the Generalized Additive Model to Evaluate the Impacts of Outdoor Ambient Environmental Factors on Scaffolding Construction Productivity |
| title | Comparing the Random Forest with the Generalized Additive Model to Evaluate the Impacts of Outdoor Ambient Environmental Factors on Scaffolding Construction Productivity |
| title_full | Comparing the Random Forest with the Generalized Additive Model to Evaluate the Impacts of Outdoor Ambient Environmental Factors on Scaffolding Construction Productivity |
| title_fullStr | Comparing the Random Forest with the Generalized Additive Model to Evaluate the Impacts of Outdoor Ambient Environmental Factors on Scaffolding Construction Productivity |
| title_full_unstemmed | Comparing the Random Forest with the Generalized Additive Model to Evaluate the Impacts of Outdoor Ambient Environmental Factors on Scaffolding Construction Productivity |
| title_short | Comparing the Random Forest with the Generalized Additive Model to Evaluate the Impacts of Outdoor Ambient Environmental Factors on Scaffolding Construction Productivity |
| title_sort | comparing the random forest with the generalized additive model to evaluate the impacts of outdoor ambient environmental factors on scaffolding construction productivity |
| url | http://purl.org/au-research/grants/arc/LP140100873 http://hdl.handle.net/20.500.11937/67848 |