Comparing the Random Forest with the Generalized Additive Model to Evaluate the Impacts of Outdoor Ambient Environmental Factors on Scaffolding Construction Productivity
Publication: Journal of Construction Engineering and Management
Volume 144, Issue 6
Abstract
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.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Data analyzed during the study were provided by a third party. Requests for data should be directed to the provider indicated in the Acknowledgements. Information about the Journal’s data sharing policy can be found here: http://ascelibrary.org/doi/10.1061/%28ASCE%29CO.1943-7862.0001263.
Acknowledgments
The records on the scaffolding project were provided by KAEFER Integrated Services Pty Ltd. This research was undertaken with the benefit of a grant from the Australian Research Council Linkage Project (No. LP140100873). Our sincere thanks also go to Ms. Angela Wilson for the proofreading she has provided.
References
Aalto, J., Pirinen, P., Heikkinen, J., and Venäläinen, A. (2013). “Spatial interpolation of monthly climate data for Finland: Comparing the performance of kriging and generalized additive models.” Theor. Appl. Climatol., 112(1–2), 99–111.
ABC (Australian Building Codes) Board. (2006). Building code of Australia, Volume 1, Class 2 to Class 9 buildings, Canberra, Australia.
Abdel-Rahman, E. M., Ahmed, F. B., and Ismail, R. (2013). “Random forest regression and spectral band selection for estimating sugarcane leaf nitrogen concentration using EO-1 Hyperion hyperspectral data.” Int. J. Remote Sens., 34(2), 712–728.
Australian Radiation Protection and Nuclear Safety Agency. (2016). “Ultraviolet radiation monitoring.” ⟨http://www.arpansa.gov.au/uvindex/index.cfm⟩ (Nov. 15, 2016).
Australian Trade Commission. (2015). “Australia benchmark report 2015.” Sydney, NSW, Australia.
Baayen, H., Vasishth, S., Kliegl, R., and Bates, D. (2017). “The cave of shadows: Addressing the human factor with generalized additive mixed models.” J. Memory Lang., 94, 206–234.
Breiman, L. (2001). “Random forests.” Mach. Learn., 45(1), 5–32.
Cohen, J., Cohen, P., West, S. G., and Aiken, L. S. (2013). Applied multiple regression/correlation analysis for the behavioral sciences, Routledge, Abingdon, U.K.
Collins, R., Zhang, S., Kim, K., and Teizer, J. (2014). “Integration of safety risk factors in BIM for scaffolding construction.” Comput. Civ. Build. Eng., 307–314.
Coulston, J. W., Blinn, C. E., Thomas, V. A., and Wynne, R. H. (2016). “Approximating prediction uncertainty for random forest regression models.” Photogram. Eng. Rem. Sens., 82(3), 189–197.
Duncan, K., Philips, P., and Prus, M. (2014). “Prevailing wage regulations and school construction costs: Cumulative evidence from British Columbia.” Ind. Relations J. Econ. Soc., 53(4), 593–616.
Gamal Aboelmaged, M., and Mohamed El Subbaugh, S. (2012). “Factors influencing perceived productivity of Egyptian teleworkers: An empirical study.” Meas. Bus. Excellence, 16(2), 3–22.
Goodrum, P. M., and Haas, C. T. (2002). “Partial factor productivity and equipment technology change at activity level in U.S. construction industry.” J. Constr. Eng. Manage., 463–472.
Grimm, C. T., and Wagner, N. K. (1974). “Weather effects on mason productivity.” J. Constr. Div., 100(3), 319–335.
Hancher, D. E., and Abd-Elkhalek, H. A. (1998). “Effect of hot weather on construction labor productivity and costs.” Cost Eng., 40(4), 32–36.
Hastie, T. J., and Tibshirani, R. J. (1990). Generalized additive models, CRC Press, Boca Raton, FL.
Hastie, T., Tibshirani, R., and Friedman, J. (2009). The elements of statistical learning: Data mining, New York, 2nd Ed., Springer, Berlin.
International Labour Organization. (2015). “Working conditions.” ⟨http://www.ilo.org/global/topics/working-conditions/lang--en/index.htm⟩ (Oct. 20, 2015).
Kadir, M. R. A., Lee, W. P., Jaafar, M. S., Sapuan, S. M., and Ali, A. A. A. (2005). “Factors affecting construction labour productivity for Malaysian residential projects.” Struct. Surv., 23(1), 42–54.
Kaming, P. F., Olomolaiye, P. O., Holt, G. D., and Harris, F. C. (1997). “Factors influencing construction time and cost overruns on high-rise projects in Indonesia.” Constr. Manage. Econ., 15(1), 83–94.
Koehn, E., and Brown, G. (1985). “Climatic effects on construction.” J. Constr. Eng. Manage., 129–137.
Lenneman, J., Schwartz, S., Giuseffi, D. L., and Wang, C. (2011). “Productivity and health: An application of three perspectives to measuring productivity.” J. Occup. Environ. Med., 53(1), 55–61.
Li, X., Chow, K. H., Zhu, Y., and Lin, Y. (2016). “Evaluating the impacts of high-temperature outdoor working environments on construction labor productivity in China: A case study of rebar workers.” Build. Environ., 95, 42–52.
Liaw, A., and Wiener, M. (2002). “Classification and regression by random forest.” R News, 2(3), 18–22.
Makulsawatudom, A., Emsley, M., and Sinthawanarong, K. (2004). “Critical factors influencing construction productivity in Thailand.” J. King Mongkut’s Univ. Technol., 14(3), 1–6.
Maloney, W. F. (1983). “Productivity improvement: The influence of labor.” J. Constr. Eng. Manage., 321–334.
Mohamed, S., and Srinavin, K. (2002). “Thermal environment effects on construction workers’ productivity.” Work Study, 51(6), 297–302.
Montgomery, D. C., Peck, E. A., and Vining, G. G. (2015). Introduction to linear regression analysis, Wiley, New York.
Moore, T. (2016). “Record highs possible in parts of the Northeast through Tuesday; Western heat expands.” Weather Underground ⟨https://www.wunderground.com/news/summer-heat-expanding-widespread-dangerous⟩.
Moselhi, O., Gong, D., and El-Rayes, K. (1997). “Estimating weather impact on the duration of construction activities.” Can. J. Civ. Eng., 24(3), 359–366.
Moselhi, O., and Khan, Z. (2010). “Analysis of labour productivity of formwork operations in building construction.” Constr. Innovation, 10(3), 286–303.
Mustapha, F. H., and Naoum, S. (1998). “Factors influencing the effectiveness of construction site managers.” Int. J. Project Manage., 16(1), 1–8.
National Electrical Contractors Association. (1974). “The effect of temperature on productivity.”, Bethesda, MD.
Oshiro, T. M., Perez, P. S., and Baranauskas, J. A. (2012). “How many trees in a random forest?” Machine Learning and Data Mining in Pattern Recognition: 8th Int. Conf., MLDM 2012, Springer, Berlin, 154.
Proverbs, D. G., Holt, G. D., and Olomolaiye, P. O. (1998). “Factors impacting construction project duration: A comparison between France, Germany and the UK.” Build. Environ., 34(2), 197–204.
Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M., and Chica-Rivas, M. (2015). “Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines.” Ore Geol. Rev., 71, 804–818.
Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M., and Rigol-Sanchez, J. P. (2012). “An assessment of the effectiveness of a random forest classifier for land-cover classification.” ISPRS J. Photogramm. Remote Sens., 67, 93–104.
Rubec, P. J., Kiltie, R., Leone, E., Flamm, R. O., McEachron, L., and Santi, C. (2016). “Using delta-generalized additive models to predict spatial distributions and population abundance of juvenile pink shrimp in Tampa Bay, Florida.” Mar. Coastal Fish., 8(1), 232–243.
Sanders, S. R., and Thomas, H. R. (1991). “Factors affecting masonry-labor productivity.” J. Constr. Eng. Manage., 626–644.
Shadish, W. R., Zuur, A. F., and Sullivan, K. J. (2014). “Using generalized additive (mixed) models to analyze single case designs.” J. School Psychol., 52(2), 149–178.
Sonmez, R., and Rowings, J. E. (1998). “Construction labor productivity modeling with neural networks.” J. Constr. Eng. Manage., 498–504.
Srinavin, K., and Mohamed, S. (2003). “Thermal environment and construction workers’ productivity: Some evidence from Thailand.” Build. Environ., 38(2), 339–345.
Thomas, H. R., Riley, D. R., and Sanvido, V. E. (1999). “Loss of labor productivity due to delivery methods and weather.” J. Constr. Eng. Manage., 39–46.
Thomas, H. R., and Sakarcan, A. S. (1994). “Forecasting labor productivity using factor model.” J. Constr. Eng. Manage., 228–239.
Thomas, H. R., and Yiakoumis, I. (1987). “Factor model of construction productivity.” J. Constr. Eng. Manage., 623–639.
Tsanas, A., and Xifara, A. (2012). “Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools.” Energy Build., 49, 560–567.
Williden, M., Schofield, G., and Duncan, S. (2012). “Establishing links between health and productivity in the New Zealand workforce.” J. Occup. Environ. Med., 54(5), 545–550.
Yi, W., and Chan, A. P. (2013). “Critical review of labor productivity research in construction journals.” J. Manage. Eng., 214–225.
Zhao, Y., and Zhang, Y. (2008). “Comparison of decision tree methods for finding active objects.” Adv. Space Res., 41(12), 1955–1959.
Zhu, X., Xue, X., Chen, M., and Zhou, H. (2013). “Mobile IT used in construction: A case for scaffolding safety management.” ICCREM 2013: Construction and Operation in the Context of Sustainability, 482–491.
Information & Authors
Information
Published In
Copyright
©2018 American Society of Civil Engineers.
History
Received: Jun 14, 2017
Accepted: Dec 5, 2017
Published online: Mar 28, 2018
Published in print: Jun 1, 2018
Discussion open until: Aug 28, 2018
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.