Variance Analysis on Regression Models for Estimating Labor Costs of Prefabricated Components
Publication: Journal of Computing in Civil Engineering
Volume 36, Issue 5
Abstract
Regression modeling has been based on analyzing error terms between the predicted output and the target output without addressing the variance of the predicted output and the impact of individual input parameters on the variance. This research critically reviews established methods for variance analysis on commonly applied multiple linear regressions (MLR). An MLR model with high accuracy (the mean of the prediction close to the target value) but low precision (too high of a variance of the prediction) would be deemed insufficient in the context of cost estimating applications. An analytical method to account for the impact of the uncertainty associated with each input parameter on the uncertainty of the final output has yet to be formalized. This research integrates the error propagation theory with MLR modeling in an attempt to quantify the variance of the MLR predicted output in estimating labor cost for prefabricated products. A metric based on the resulting variance analysis (i.e., the ratio of the standard deviation over the mean) is found effective to gauge the precision of the MLR model. The research has advanced regression modeling methods with respect to MLR variance analysis and contributed to the estimating practice for prefabricated products such as structural steel fabrication, precast concrete, industrial modules, and building modules.
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Data Availability Statement
The model or code that supports the findings of this study is available from the corresponding author upon reasonable request.
Acknowledgments
The research was substantially funded by the Canadian Precast Concrete Institute (CPCI) and Mathematics of Information Technology and Complex Systems (MITACS) through an Accelerate Internship program (Application Reference IT16280). The authors would like to acknowledge Robert Burak and Val Sylaj from CPCI, Kevin Kooiker and Jeff Church from Eagle Builders, and Jason Rabasse from Lafarge Precast for facilitating the research.
References
Amiri-Simkooei, A. R., F. Zangeneh-Nejad, and J. Asgari. 2016. “On the covariance matrix of weighted total least-squares estimates.” J. Surv. Eng. 142 (3): 04015014. https://doi.org/10.1061/(ASCE)SU.1943-5428.0000153.
Barrett, B. E., and J. B. Gray. 1994. “A computational framework for variable selection in multivariate regression.” Stat. Comput. 4 (3): 203–212. https://doi.org/10.1007/BF00142572.
Barrie, D. S., and B. C. Paulson. 2001. Professional construction management: Including CM, design-construct, and general contracting: Series in construction engineering and project management. 3rd ed. New York: McGraw-Hill.
Bayram, S., and S. Al-Jibouri. 2016. “Efficacy of estimation methods in forecasting building projects’ costs.” J. Constr. Eng. Manage. 142 (11): 05016012. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001183.
Blismas, N., C. Pasquire, and A. Gibb. 2006. “Benefit evaluation for offsite production in construction.” Construct. Manage. Econ. 24 (2): 121–130. https://doi.org/10.1080/01446190500184444.
Borjeghaleh, R. M., and J. M. Sardroud. 2016. “Approaching industrialization of buildings and integrated construction using building information modeling.” Procedia Eng. 164: 534–541. https://doi.org/10.1016/j.proeng.2016.11.655.
Cantoni, M., E. Weyer, Y. Li, S. K. Ooi, I. Mareels, and M. Ryan. 2007. “Control of large-scale irrigation networks.” Proc. IEEE 95 (1): 75–91. https://doi.org/10.1109/JPROC.2006.887289.
Cao, Y., B. Ashuri, and M. Baek. 2018. “Prediction of unit price bids of resurfacing highway projects through ensemble machine learning.” J. Comput. Civ. Eng. 32 (5): 04018043. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000788.
Chan, S. L., and M. Park. 2005. “Project cost estimation using principal component regression.” Construct. Manage. Econ. 23 (3): 295–304. https://doi.org/10.1080/01446190500039812.
Cho, Y., C. T. Haas, S. V. Sreenivasan, and K. Liapi. 2004. “Position error modeling for automated construction manipulators.” J. Constr. Eng. Manage. 130 (1): 50–58. https://doi.org/10.1061/(ASCE)0733-9364(2004)130:1(50).
Choi, K., Y. H. Kim, J. Bae, and H. W. Lee. 2015. “Determining future maintenance costs of low-volume highway rehabilitation projects for incorporation into life-cycle cost analysis.” J. Comput. Civ. Eng. 30 (4): 04015055. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000533.
Chou, J., C. Chiu, M. Farfoura, and I. Al-Taharwa. 2011. “Optimizing the prediction accuracy of concrete compressive strength based on a comparison of data-mining techniques.” J. Comput. Civ. Eng. 25 (3): 242–253. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000088.
Creedy, G. D., M. Skitmore, and J. K. W. Wong. 2010. “Evaluation of risk factors leading to cost overrun in delivery of highway construction projects.” J. Constr. Eng. Manage. 136 (5): 528–537. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000160.
El-Abbasy, M. S., A. Senouci, T. Zayed, F. Mirahadi, and L. Parvizsedghy. 2014. “Condition prediction models for oil and gas pipelines using regression analysis.” J. Constr. Eng. Manage. 140 (6): 04014013. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000838.
Elhag, T. M. S., and Y. Wang. 2007. “Risk assessment for bridge maintenance projects: Neural networks versus regression techniques.” J. Comput. Civ. Eng. 21 (6): 402–409. https://doi.org/10.1061/(ASCE)0887-3801(2007)21:6(402).
Enshassi, A., S. Mohamed, and M. Abdel-Hadi. 2013. “Factors affecting the accuracy of pre-tender cost estimates in the Gaza strip.” Constr. Dev. Countries 18 (1): 73–94.
Hasan, M., and M. Lu. 2017. “Error quantification and visualization in using sensors to position backhoe excavator.” In Computing in civil engineering: Smart safety, sustainability, and resilience, edited by K. Y. Lin, N. El-Gohery, and P. Tang, 150–157. Reston, VA: ASCE.
Hasan, M., and M. Lu. 2019. “Planning and scheduling bridge girders fabrication through shop-floor operations simulation.” In Vol. 1 of Proc., 2019 European Conf. on Computing in Construction, 75–84. Cambridge, UK: European Council on Computing in Construction. https://doi.org/10.35490/ec3.2019.162.
Hasan, M., and M. Lu. 2021. “Error propagation model for analyzing project labor cost budget risks in industrial construction.” J. Constr. Eng. Manage. 147 (4): 04021007. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002010.
Huang, C. H., and S. H. Hsieh. 2020. “Predicting BIM labor cost with random forest and simple linear regression.” Autom. Constr. 118 (Oct): 103280. https://doi.org/10.1016/j.autcon.2020.103280.
Kim, G. H., S. H. An, and K. I. Kang. 2004. “Comparison of construction cost estimating models based on regression analysis, neural networks, and case-based reasoning.” Build. Environ. 39 (10): 1235–1242. https://doi.org/10.1016/j.buildenv.2004.02.013.
Koch, K. R., H. Kuhlmann, and W. D. Schuh. 2010. “Approximating covariance matrices estimated in multivariate models by estimated auto- and cross-covariances.” J. Geod. 84 (6): 383–397. https://doi.org/10.1007/s00190-010-0375-5.
Lee, J., Y. J. Park, C. H. Choi, and C. H. Han. 2017. “BIM-assisted labor productivity measurement method for structural formwork.” Autom. Constr. 84 (Dec): 121–132. https://doi.org/10.1016/j.autcon.2017.08.009.
Lingras, P., and M. Adamo. 1996. “Average and peak traffic volumes: Neural nets, regression, factor approaches.” J. Comput. Civ. Eng. 10 (4): 300–306. https://doi.org/10.1061/(ASCE)0887-3801(1996)10:4(300).
Liu, J., M. F. F. Siu, and M. Lu. 2016. “Modular construction system simulation incorporating off-shore fabrication and multi-mode transportation.” In Proc., Winter Simulation Conf. 2016, 3269–3280. New York: IEEE. https://doi.org/10.1109/WSC.2016.7822358.
Liu, Z., R. Sadiq, B. Rajani, and H. Najjaran. 2010. “Exploring the relationship between soil properties and deterioration of metallic pipes using predictive data mining methods.” J. Comput. Civ. Eng. 24 (3): 289–301. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000032.
Lowe, D. J., M. W. Emsley, and A. Harding. 2006. “Predicting construction cost using multiple regression techniques.” J. Constr. Eng. Manage. 132 (7): 750–758. https://doi.org/10.1061/(ASCE)0733-9364(2006)132:7(750).
Lu, M., M. Soleimanifar, and A. Mohsenijam. 2017. “Practical framework for estimating and planning engineered material fabrication: Case of piping spools.” In Proc., 2017 Int. Conf. on Business, Big-Data, and Decision Sciences (ICBBD2017), 41–48. Bangkok, Thailand: Chulalongkorn Univ.
Lu, X., W. Zhou, X. Ding, X. Shi, B. Luan, and M. Li. 2019. “Ensemble learning regression for estimating unconfined compressive strength of cemented paste backfill.” IEEE Access 7: 72125–72133. https://doi.org/10.1109/ACCESS.2019.2918177.
Marchionni, V., M. Cabral, C. Amado, and D. Covas. 2016. “Estimating water supply infrastructure cost using regression techniques.” J. Water Resour. Plann. Manage. 142 (4): 04016003. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000627.
Mohsenijam, A., and M. Lu. 2019. “Framework for developing labour-hour prediction models from project design features: Case study in structural steel fabrication.” Can. J. Civ. Eng. 46 (10): 871–880. https://doi.org/10.1139/cjce-2018-0349.
Mohsenijam, A., M. F. Siu, and M. Lu. 2017. “Modified stepwise regression approach to streamlining predictive analytics for construction engineering applications.” J. Comput. Civ. Eng. 31 (3): 04016066. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000636.
Olive, D. J. 2007. “Prediction intervals for regression models.” Comput. Stat. Data Anal. 51 (6): 3115–3122. https://doi.org/10.1016/j.csda.2006.02.006.
Olive, D. J. 2017. Linear regression. New York: Springer.
Parker, A. D., D. S. Barrie, and R. M. Snyder. 1984. Planning and estimating heavy construction. New York: McGraw-Hill.
PMI (Project Management Institute). 2017. A guide to the project management body of knowledge (PMBOK guide). 6th ed. Newtown Square, PA: PMI.
Portas, J., and S. AbouRizk. 1997. “Neural network model for estimating construction productivity.” J. Constr. Eng. Manage. 123 (4): 399–410. https://doi.org/10.1061/(ASCE)0733-9364(1997)123:4(399).
Trost, S. M., and G. D. Oberlender. 2003. “Predicting accuracy of early cost estimates using factor analysis and multivariate regression.” J. Constr. Eng. Manage. 129 (2): 198–204. https://doi.org/10.1061/(ASCE)0733-9364(2003)129:2(198).
Wai-Lok Lai, W., X. Dérobert, and P. Annan. 2018. “A review of ground penetrating radar application in civil engineering: A 30-year journey from locating and testing to imaging and diagnosis.” NDT E Int. 96 (Jun): 58–78. https://doi.org/10.1016/j.ndteint.2017.04.002.
Xue, J., Y. Leung, and J. H. Ma. 2015. “High-order Taylor series expansion methods for error propagation in geographic information systems.” J. Geog. Syst. 17 (2): 187–206. https://doi.org/10.1007/s10109-014-0207-x.
Zayed, T. M., and D. W. Halpin. 2005. “Productivity and cost regression models for pile construction.” J. Constr. Eng. Manage. 131 (7): 779–789. https://doi.org/10.1061/(ASCE)0733-9364(2005)131:7(779).
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Published In
Journal of Computing in Civil Engineering
Volume 36 • Issue 5 • September 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Dec 19, 2021
Accepted: Apr 12, 2022
Published online: Jun 15, 2022
Published in print: Sep 1, 2022
Discussion open until: Nov 15, 2022
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