Bayesian-Based Hybrid Simulation Approach to Project Completion Forecasting for Underground Construction
Publication: Journal of Construction Engineering and Management
Volume 140, Issue 1
Abstract
Real-time simulation is powerful in forecasting the completion probability of long-term projects with repetitive tasks but fails to consider the time-varying uncertainty of inputs caused by construction process variabilities. In this paper, an improved method is introduced for predicting the time-varying probability of project completion of ongoing underground cavern group projects using Bayesian updating techniques. Within a tailor-made hierarchical simulation model, the Bayesian approach is adopted to constantly update duration distributions of unfinished project activities according to onsite data. The probability of project completion can therefore be increasingly refined during the process. The methodology is further explained in a case study where its feasibility and advantage over traditional approaches are verified. The success may also be replicated in addressing other similar time-varying uncertainty issues inherently present in almost all construction projects.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The authors gratefully acknowledge the support of the State Key Laboratory of Hydraulic Engineering Simulation and Safety (Tianjin University), the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (51021004), and the Tianjin Research Program of Application Foundation and Advanced Technology (13JCYBJC19400).
References
AbouRizk, S. M., and Halpin, D. W. (1992). “Statistical properties of construction duration data.” J. Constr. Eng. Manage., 118(3), 525–544.
AbouRizk, S. M., Halpin, D. W., and Lutz, J. D. (1992). “State of the art in construction simulation.” Proc., 24th Winter Simulation Conf., Association for Computing Machinery, New York, 1271–1277.
Ahuja, H., Dozzi, S. P., and AbouRizk, S. M. (1995). Project management techniques in planning and controlling construction projects, 2nd Ed., Wiley, New York.
Ang, A. H-S., and Tang, W. H. (1975). Probability concepts in engineering planning and design, Vol. 1, Wiley, New York.
Box, G. E. P., and Tiao, G. C. (1992). Bayesian inference in statistical analysis, Addison-Wesley, Boston.
Callahan, M. T., Quackenbush, D. G., and Rowings, J. E. (1992). Construction project scheduling, McGraw-Hill, New York.
Chung, T. H., Mohamed, Y., and AbouRizk, S. M. (2004). “Simulation input updating using Bayesian techniques.” Proc., 36th Winter Simulation Conf., Vol. 2, IEEE, New York, 1238–1243.
Chung, T. H., Mohamed, Y., and AbouRizk, S. M. (2006). “Bayesian updating application into simulation in the North Edmonton sanitary trunk tunnel project.” J. Constr. Eng. Manage., 132(8), 882–894.
Fahidy, T. Z. (2004). “Bayesian updating of electrochemical process parameters via natural conjugate probability distributions.” Electrochim. Acta, 49(27), 5013–5021.
Fink, D. (1997). “A compendium of conjugate priors.”, Montana State Univ., Bozeman, MT.
Gardoni, P., Reinschmidt, K. F., and Kumar, R. (2007). “A probabilistic framework for Bayesian adaptive forecasting of project progress.” Comput. Aided Civ. Infrastruct. Eng., 22(3), 182–196.
Gelman, A., Carlin, J. B., Stern, H. S., and Rubin, D. B. (2003). Bayesian data analysis, 2nd Ed., Chapman and Hall/CRC, Boca Raton, FL.
Haas, C., and Einstein, H. H. (2002). “Updating the decision aids for tunneling.” J. Constr. Eng. Manage., 128(1), 40–48.
Halpin, D. W. (1998). “Construction simulation: A status report.” Proc., 5th Canadian Construction Research Forum, Univ. of Alberta and Alberta Construction Industry, Alberta, Canada, 33–42.
Halpin, D. W., and Riggs, L. S. (1992). Planning and analysis of construction operations, Wiley, New York.
Kim, B. C., and Reinschmidt, K. F. (2007). “An S-curve Bayesian model for forecasting probabilistic distributions on project duration and cost at completion.” Proc., 25th Conf. of Constr. Manage. and Economics: Past, Present, and Future, Vol. 1, Univ. of Reading, Reading, U.K., 136.
Kim, B. C., and Reinschmidt, K. F. (2009). “Probabilistic forecasting of project duration using Bayesian inference and the beta distribution.” J. Constr. Eng. Manage., 135, 178–186.
Lee, D.-E. (2005). “Probability of project completion using stochastic project scheduling simulation (SPSS).” J. Constr. Eng. Manage., 131(3), 310–318.
Li, J. R., and Zhong, D. H. (2005). “A GIS-based visual simulation system for underground power station construction.” Proc., Int. Conf. on Computing in Civil Engineering, ASCE, Reston, VA, 1–12.
Min, S. Y., Einstein, H. H., Lee, J. S., and Kim, T. K. (2003). “Application of decision aids for tunneling (DAT) to a drill and blast tunnel.” KSCE J. Civ. Eng., 7(5), 619–628.
Senior, B. A., and Halpin, D. W. (1998). “Simplified simulation system for construction projects.” J. Constr. Eng. Manage., 124(1), 72–81.
Song, L., and Eldin, N. N. (2012). “Adaptive real-time tracking and simulation of heavy construction operations for look-ahead scheduling.” Autom. Constr., 27, 32–39.
Touran, A. (1987). “Simulation of tunnel operation.” J. Constr. Eng. Manage., 113(4), 554–568.
Weinberg, G. H., and Shumaker, J. A. (1974). Statistics: An intuitive approach, 4th Ed., Broks/Cole, Belmont, CA.
Zhong, D. H., Li, J. R., Zhu, H. R., and Song, L. G. (2004). “Geographic information system-based visual simulation methodology and its application in concrete dam construction processes.” J. Constr. Eng. Manage., 130(5), 742–750.
Information & Authors
Information
Published In
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Apr 25, 2013
Accepted: Jun 20, 2013
Published online: Aug 29, 2013
Published in print: Jan 1, 2014
Discussion open until: Jan 29, 2014
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.