Planning Construction Projects in Deep Uncertainty: A Data-Driven Uncertainty Analysis Approach
Publication: Journal of Construction Engineering and Management
Volume 148, Issue 8
Abstract
Construction planning is significantly affected by many uncertain factors derived from construction tasks, the environments, resources, technologies, personnel, and more. Uncertainty analysis approaches are thus critical to supporting the decision making associated with construction planning. However, the precise probability distributions (PDs) of uncertain factors are sometimes inaccessible, especially for construction projects in a novel context with limited previous experiences or similar references. These situations constitute a deep uncertainty problem, and probability-based methods are no longer applicable for construction planning. To address this challenge, an uncertainty analysis approach that integrates Latin hypercube sampling (LHS), discrete-event simulation (DES), and the patient rule induction method (PRIM) is proposed. Specifically, it is progressed by LHS and DES to generate a wide array of uncertainty scenarios represented by possible PDs to quantify the robustness of various construction decisions; then, PRIM is used to identify the vulnerable scenarios that will jeopardize project completion. The approach was implemented on a real-world project, and the results demonstrated that it was able to identify the most robust construction schemes and vulnerable scenarios for construction planning. This research contributes a data-driven technology that provides an uncertainty analysis approach for construction planning without relying on assumed probability distributions from limited, unreliable project references.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This project was funded by the National Natural Science Foundation of China (52108279), the China Postdoctoral Science Foundation (Grant No. 2020M670918), the Swedish Research Council for Environment, Agricultural Sciences, and Spatial Planning (Formas Grant No. 2016-20071), and the Key Technology Research on Intelligent Construction of Prefabricated Subway Stations, a science and technology research project of China State Construction Third Urban Construction Co., Ltd.
References
AbouRizk, S., D. Halpin, Y. Mohamed, and U. Hermann. 2011. “Research in modeling and simulation for improving construction engineering operations.” J. Constr. Eng. Manage. 137 (10): 843–852. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000288.
Alzraiee, H., T. Zayed, and O. Moselhi. 2015. “Dynamic planning of construction activities using hybrid simulation.” Autom. Constr. 49 (Jan): 176–192. https://doi.org/10.1016/j.autcon.2014.08.011.
Banks, J. 1998. Handbook of simulation: Principles, methodology, advances, applications, and practice. Hoboken, NJ: Wiley.
Beh, E. H., F. Zheng, G. C. Dandy, H. R. Maier, and Z. Kapelan. 2017. “Robust optimization of water infrastructure planning under deep uncertainty using metamodels.” Environ. Modell. Software 93 (Jul): 92–105. https://doi.org/10.1016/j.envsoft.2017.03.013.
Bertsimas, D., V. Gupta, and N. Kallus. 2018. “Data-driven robust optimization.” Math. Program. 167 (2): 235–292. https://doi.org/10.1007/s10107-017-1125-8.
Buertey, J. I. T., E. Abeere-Inga, and T. A. Kumi. 2012. “Estimating cost contingency for construction projects: The challenge of systemic and project specific risk.” J. Constr. Project Manage. Innovation 2 (1): 166–189.
Cavalcante, I. M., E. M. Frazzon, F. A. Forcellini, and D. Ivanov. 2019. “A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing.” Int. J. Inf. Manage. 49 (Dec): 86–97. https://doi.org/10.1016/j.ijinfomgt.2019.03.004.
CECMSS (Construction Engineering Cost Management Station Shenzhen). 2016. Construction engineering quota of Shenzhen. [In Chinese.] Shenzhen, China: China Building Industry Press.
Chung, T. H., Y. Mohamed, and S. AbouRizk. 2004. “Simulation input updating using Bayesian techniques.” In Proc., 2004 Winter Simulation Conf., 1238–1243. New York: IEEE.
Clark, M., and R. Pulwarty. 2003. “Devising resilient responses to potential climate change impacts.” Accessed July 1, 2021. https://sciencepolicy.colorado.edu/ogmius/archives/issue_5/exchange1.html.
CURT (Construction Users Roundtable). 2004. Collaboration, integrated information, and the project lifecycle in building design, construction and operation. Cincinnati: CURT.
Dixon, L., R. J. Lempert, T. LaTourrette, and R. T. Reville. 2007. The federal role in terrorism insurance: Evaluating alternatives in an uncertain world. Santa Monica, CA: Rand Corporation.
Feng, K., W. Lu, T. Olofsson, S. Chen, H. Yan, and Y. Wang. 2018. “A predictive environmental assessment method for construction operations: Application to a northeast China case study.” Sustainability 10 (11): 3868. https://doi.org/10.3390/su10113868.
Fishman, G. S. 2013. Discrete-event simulation: Modeling, programming, and analysis. New York: Springer.
Friedman, J. H., and N. I. Fisher. 1999. “Bump hunting in high-dimensional data.” Stat. Comput. 9 (2): 123–143. https://doi.org/10.1023/A:1008894516817.
Giudici, F., A. Castelletti, M. Giuliani, and H. R. Maier. 2020. “An active learning approach for identifying the smallest subset of informative scenarios for robust planning under deep uncertainty.” Environ. Modell. Software 127 (May): 104681. https://doi.org/10.1016/j.envsoft.2020.104681.
Goedkoop, M., R. Heijungs, M. Huijbregts, A. De Schryver, J. Struijs, and R. Van Zelm. 2009. “A life cycle impact assessment method which comprises harmonised category indicators at the midpoint and the endpoint level.” Accessed January 6, 2009. https://www.leidenuniv.nl/cml/ssp/publications/recipe_characterisation.pdf.
Hanna, A. S., and M. A. Skiffington. 2010. “Effect of preconstruction planning effort on sheet metal project performance.” J. Constr. Eng. Manage. 136 (2): 235–241. https://doi.org/10.1061/(ASCE)0733-9364(2010)136:2(235).
Hong, J., G. Q. Shen, Y. Peng, Y. Feng, and C. Mao. 2016. “Uncertainty analysis for measuring greenhouse gas emissions in the building construction phase: A case study in China.” J. Cleaner Prod. 129 (Aug): 183–195. https://doi.org/10.1016/j.jclepro.2016.04.085.
Huang, H., H. Wan, and J. Han. 2001. “Arranging the transient process is an effective method Improved the ‘robustness, adaptability and stability’ of closed-loop system.” [In Chinese.] Supplement, Control Theory Appl. 18 (S1): 89.
Hulett, D. T., and W. T. Whitehead. 2016. “The Monte Carlo method for modeling & mitigating systemic risk.” In Proc., 2016 AACE Int. Annual Meeting. Morgantown, WV: Association for the Advancement of Cost Engineering International.
Ji, W., and S. AbouRizk. 2018. “Data-driven simulation model for quality-induced rework cost estimation and control using absorbing Markov chains.” J. Constr. Eng. Manage. 144 (8): 04018078. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001534.
Ji, W., Y. Li, and S. AbouRizk. 2019. “Integrated data-driven approach for analyzing pipe welding operator-quality performance.” Autom. Constr. 106 (Oct): 102814. https://doi.org/10.1016/j.autcon.2019.04.009.
Kasprzyk, J. R., S. Nataraj, P. M. Reed, and R. J. Lempert. 2013. “Many objective robust decision making for complex environmental systems undergoing change.” Environ. Modell. Software 42 (Apr): 55–71. https://doi.org/10.1016/j.envsoft.2012.12.007.
Kim, B.-C., and K. F. Reinschmidt. 2009. “Probabilistic forecasting of project duration using Bayesian inference and the beta distribution.” J. Constr. Eng. Manage. 135 (3): 178–186. https://doi.org/10.1061/(ASCE)0733-9364(2009)135:3(178).
Kim, T., Y.-W. Kim, and H. Cho. 2020. “Dynamic production scheduling model under due date uncertainty in precast concrete construction.” J. Cleaner Prod. 257 (Jun): 120527. https://doi.org/10.1016/j.jclepro.2020.120527.
Kotlarski, S., P. Szabó, S. Herrera, O. Räty, K. Keuler, P. M. Soares, R. M. Cardoso, T. Bosshard, C. Pagé, and F. Boberg. 2019. “Observational uncertainty and regional climate model evaluation: A pan-European perspective.” Int. J. Climatol. 39 (9): 3730–3749. https://doi.org/10.1002/joc.5249.
Kwakkel, J. H. 2017. “The exploratory modeling workbench: An open source toolkit for exploratory modeling, scenario discovery, and (multi-objective) robust decision making.” Environ. Modell. Software 96 (Oct): 239–250. https://doi.org/10.1016/j.envsoft.2017.06.054.
Larsson, J., W. Lu, J. Krantz, and T. Olofsson. 2016. “Discrete event simulation analysis of product and process platforms: A bridge construction case study.” J. Constr. Eng. Manage. 142 (4): 04015097. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001093.
Lau, S. E. N., R. Zakaria, E. Aminudin, C. C. Saar, A. Yusof, and C. M. F. H. C. Wahid. 2018. “A review of application building information modeling (BIM) during pre-construction stage: Retrospective and future directions.” IOP Conf. Ser.: Earth Environ. Sci. 143 (1): 012050. https://doi.org/10.1088/1755-1315/143/1/012050.
Lempert, R. J., S. W. Popper, and S. C. Bankes. 2003. Shaping the next one hundred years: New methods for quantitative, long-term policy analysis. Santa Monica, CA: Rand Corporation.
Li, Y., X. Zhang, G. Ding, and Z. Feng. 2016. “Developing a quantitative construction waste estimation model for building construction projects.” Resour. Conserv. Recycl. 106 (Jan): 9–20. https://doi.org/10.1016/j.resconrec.2015.11.001.
Liu, J., and M. Lu. 2018. “Constraint programming approach to optimizing project schedules under material logistics and crew availability constraints.” J. Constr. Eng. Manage. 144 (7): 04018049. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001507.
Luo, L., Q. He, E. J. Jaselskis, and J. Xie. 2017. “Construction project complexity: Research trends and implications.” J. Constr. Eng. Manage. 143 (7): 04017019. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001306.
Maass, W., J. Parsons, S. Purao, V. C. Storey, and C. Woo. 2018. “Data-driven meets theory-driven research in the era of big data: Opportunities and challenges for information systems research.” J. Assoc. Inf. Syst. 19 (12): 1253–1273. https://doi.org/10.17705/1jais.00526.
McInerney, D., R. Lempert, and K. Keller. 2012. “What are robust strategies in the face of uncertain climate threshold responses?” Clim. Change 112 (3): 547–568. https://doi.org/10.1007/s10584-011-0377-1.
Mcphail, C., H. Maier, J. Kwakkel, M. Giuliani, A. Castelletti, and S. Westra. 2018. “Robustness metrics: How are they calculated, when should they be used and why do they give different results?” Eart’s Future 6 (2): 169–191. https://doi.org/10.1002/2017EF000649.
Mohamed, Y., and S. AbouRizk. 2006. “A hybrid approach for developing special purpose simulation tools.” Can. J. Civ. Eng. 33 (12): 1505–1515. https://doi.org/10.1139/l06-073.
MOHURD (Ministry of Housing and Urban-Rural Development). 2012. Construction equipment engineering quota of China. [In Chinese.] Beijing: MOHURD.
Moret, Y., and H. H. Einstein. 2016. “Construction cost and duration uncertainty model: Application to high-speed rail line project.” J. Constr. Eng. Manage. 142 (10): 05016010. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001161.
Nasir, N., M. N. M. Nawi, and A. Tapa. 2016. “Dilemma in decision making at pre-construction phase-A state of art review.” In Proc., AIP Conf., 020080. College Park, MD: American Institute of Physics.
Pachauri, R. K., L. Gomez-Echeverri, and K. Riahi. 2014. “Summary for policy makers.” In Climate change 2014: Mitigation of climate change. Cambridge: Cambridge University Press.
Pender, S. 2001. “Managing incomplete knowledge: Why risk management is not sufficient.” Int. J. Project Manage. 19 (2): 79–87. https://doi.org/10.1016/S0263-7863(99)00052-6.
PMI (Project Management Institute). 2013. A guide to the project management body of knowledge. Newtown Square, PA: PMI.
Qiao, Y., S. Labi, and J. D. Fricker. 2019. “Hazard-based duration models for predicting actual duration of highway projects using nonparametric and parametric survival analysis.” J. Manage. Eng. 35 (6): 04019024. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000700.
Rezakhani, P., and M. Maghiar. 2019. “Fuzzy analytical solution for activity duration estimation under uncertainty.” ASCE-ASME J. Risk Uncertainty Eng. Syst. Part A: Civ. Eng. 5 (4): 04019014. https://doi.org/10.1061/AJRUA6.0001020.
Ruckert, K. L., V. Srikrishnan, and K. Keller. 2019. “Characterizing the deep uncertainties surrounding coastal flood hazard projections: A case study for Norfolk, VA.” Sci. Rep. 9 (1): 1–12. https://doi.org/10.1038/s41598-019-47587-6.
Rui, K., G. Wenjun, and C. Yunxia. 2020. “Model-driven degradation modeling approaches: Investigation and review.” Chin. J. Aeronaut. 33 (4): 1137–1153. https://doi.org/10.1016/j.cja.2019.12.006.
Sadeghi, N., A. R. Fayek, and W. Pedrycz. 2010. “Fuzzy Monte Carlo simulation and risk assessment in construction.” Comput.-Aided Civ. Infrastruct. Eng. 25 (4): 238–252. https://doi.org/10.1111/j.1467-8667.2009.00632.x.
Sadeghi, N., A. R. Fayek, and N. G. Seresht. 2015. “Queue performance measures in construction simulation models containing subjective uncertainty.” Autom. Constr. 60 (Dec): 1–11. https://doi.org/10.1016/j.autcon.2015.07.023.
Salimi, S., M. Mawlana, and A. Hammad. 2018. “Performance analysis of simulation-based optimization of construction projects using high performance computing.” Autom. Constr. 87 (Mar): 158–172. https://doi.org/10.1016/j.autcon.2017.12.003.
Saltelli, A., K. Chan, and E. M. Scott. 2000. Sensitivity analysis. New York: Wiley.
Sezer, A. A., and A. Fredriksson. 2021. “Paving the path towards efficient construction logistics by revealing the current practice and Issues.” Logistics 5 (3): 53. https://doi.org/10.3390/logistics5030053.
Shortridge, J., T. Aven, and S. Guikema. 2017. “Risk assessment under deep uncertainty: A methodological comparison.” Reliab. Eng. Syst. Saf. 159 (Mar): 12–23. https://doi.org/10.1016/j.ress.2016.10.017.
Shrestha, P., and A. H. Behzadan. 2018. “Chaos theory–inspired evolutionary method to refine imperfect sensor data for data-driven construction simulation.” J. Constr. Eng. Manage. 144 (3): 04018001. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001441.
Sriver, R. L., R. J. Lempert, P. Wikman-Svahn, and K. Keller. 2018. “Characterizing uncertain sea-level rise projections to support investment decisions.” PLoS One 13 (2): e0190641. https://doi.org/10.1371/journal.pone.0190641.
STC (Shenzhen Transport Committee). 2015. Shenzhen transport annual report 2015. [In Chinese.] Shenzhen, China: STC.
SWCT (Shanghai Wind Chaser Team). 2020. “Overview of typhoon in Guangdong province.” [In Chinese.] Accessed April 10, 2020. http://www.stwc.icoc.cc/h-col-175.html.
Tam, V. W., L. Shen, and C. M. Tam. 2007. “Assessing the levels of material wastage affected by sub-contracting relationships and projects types with their correlations.” Build. Environ. 42 (3): 1471–1477. https://doi.org/10.1016/j.buildenv.2005.12.023.
Tegeltija, M., J. Oehmen, I. Kozine, and J. Geraldi. 2016. “Post-probabilistic uncertainty quantification: Discussion of potential use in product development risk management.” In Proc., DESIGN 2016 14th Int. Design Conf., 533–542. Glasgow, UK: Design Society.
Tolk, A., and C. D. Turnitsa. 2007. “Conceptual modeling of information exchange requirements based on ontological means.” In Proc., 2007 Winter Simulation Conf., 1100–1107. New York: IEEE.
Uusitalo, L., A. Lehikoinen, I. Helle, and K. Myrberg. 2015. “An overview of methods to evaluate uncertainty of deterministic models in decision support.” Environ. Modell. Software 63 (Jan): 24–31. https://doi.org/10.1016/j.envsoft.2014.09.017.
Walker, W. E., R. J. Lempert, and J. H. Kwakkel. 2013. “Deep uncertainty.” In Encyclopedia of operations research and management science, edited by S. I. Gass and M. C. Fu. Boston: Springer.
Wang, C., S. Zhang, C. Du, F. Pan, and L. Xue. 2016. “A real-time online structure-safety analysis approach consistent with dynamic construction schedule of underground caverns.” J. Constr. Eng. Manage. 142 (9): 04016042. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001153.
Wang, Y., K. Feng, and W. Lu. 2017. “An environmental assessment and optimization method for contractors.” J. Cleaner Prod. 142 (Jan): 1877–1891. https://doi.org/10.1016/j.jclepro.2016.11.097.
Wong, T. E., and K. Keller. 2017. “Deep uncertainty surrounding coastal flood risk projections: A case study for New Orleans.” Earth’s Future 5 (10): 1015–1026. https://doi.org/10.1002/2017EF000607.
Wu, L., W. Ji, and S. M. AbouRizk. 2020. “Bayesian inference with Markov chain Monte Carlo–based numerical approach for input model updating.” J. Comput. Civ. Eng. 34 (1): 04019043. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000862.
Xialu, L., W. Hongtao, C. Jian, H. Qin, Z. Hao, J. Rui, C. Xuexue, and H. Ping. 2010. “Method and basic model for development of Chinese reference life cycle database of fundamental industries.” [In Chinese.] Acta Sci. Circumstantiae 30 (10): 2136–2144.
Xue, X., and L. Wang. 2018. “The evolution and development paths of the megaproject management theory.” [In Chinese.] J. Syst. Manage. 27 (1): 22.
Yang, D. Y., and D. M. Frangopol. 2020. “Risk-based portfolio management of civil infrastructure assets under deep uncertainties associated with climate change: A robust optimisation approach.” Struct. Infrastruct. Eng. 16 (4): 531–546. https://doi.org/10.1080/15732479.2019.1639776.
Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 148 • Issue 8 • August 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Aug 8, 2021
Accepted: Mar 17, 2022
Published online: May 17, 2022
Published in print: Aug 1, 2022
Discussion open until: Oct 17, 2022
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.
Cited by
- Zhongze Yang, Weisheng Lu, Lean Modular Integrated Construction Production Phase Planning under Uncertainties: A Big Data–Driven Optimization Approach, Journal of Construction Engineering and Management, 10.1061/JCEMD4.COENG-14420, 150, 6, (2024).
- Shuo Wang, Kailun Feng, Yaowu Wang, Modeling Performance and Uncertainty of Construction Planning under Deep Uncertainty: A Prediction Interval Approach, Buildings, 10.3390/buildings13010254, 13, 1, (254), (2023).