Estimating Project S-Curves Using Polynomial Function and Neural Networks
Publication: Journal of Construction Engineering and Management
Volume 135, Issue 3
Abstract
The S-curve is a graphical representation of a construction project’s cumulative progress from start to finish. While S-curves for project control during construction should be estimated analytically based on a schedule of activity times, empirical estimation methods using various mathematical S-curve formulas have been developed for initial planning at predesign stages, with the mean for past similar projects often used as the basis of prediction. In an attempt to make an improvement, a succinct cubic polynomial function for generalizing S-curves is proposed and a comparison with existing formulas shows its advantages of accuracy and simplicity. Based on an analysis of the attributes and actual progress of 101 projects, four factors, i.e., contract amount, duration, type of work, and location, are then used as the inputs of a model developed for estimating S-curves as represented by the polynomial parameters. For model development, it is proposed to use neural networks for their ability to perform complex nonlinear mapping. The neural network model is compared with statistical models with respect to modeling and testing accuracy. The results show that the presented methodology can achieve error reduction consistently, thereby being potentially useful for owners and contractors in early financial planning and checking schedule-based estimates.
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References
Blyth, K., and Kaka, A. P. (2006). “A novel multiple linear regression model for forecasting S-curves.” Eng., Constr., Archit. Manage., 13(1), 82–95.
Chao, L. C. (2001). “Assessing earth-moving operation capacity by neural network-based simulation with physical factors.” Comput. Aided Civ. Infrastruct. Eng., 16(4), 287–294.
Chao, L.-C., and Skibniewski, M. J. (1995). “Neural network method of estimating construction technology acceptability.” J. Constr. Eng. Manage., 121(1), 130–142.
Chen, H.-F. (2003). “A study on the base of construction progress evaluation using banana curves.” MS thesis, Dept. of Civil Engineering, National Central Univ., Taiwan.
Evans, R. C., and Kaka, A. P. (1998). “Analysis of the accuracy of standard/average value curves using food retail building projects as case studies.” Eng., Constr., Archit. Manage., 5(1), 58–67.
Kaka, A. P. (1999). “The development of a benchmark model that uses historical data for monitoring the progress of current construction projects.” Eng., Constr., Archit. Manage., 6(3), 256–266.
Kaka, A. P., and Price, A. D. F. (1993). “Modeling standard cost commitment curves for contractors’ cash flow forecasting.” Constr. Manage. Econom., 11(4), 271–283.
Kenley, R., and Wilson, O. D. (1986). “A construction project cash flow model—An idiographic approach.” Constr. Manage. Econom., 4(3), 213–232.
Kim, Y., and Ballard, G. (2000). “Is the earned-value method an enemy of work flow?” Proc. 8th Annual Conf. of the Int. Group for Lean Construction, IGLC-6, ⟨http://www.iglc.net/conferences/2000/papers/⟩.
MATLAB. (2007). “Neural network toolbox for use with MATLAB 7.4.0, R2007a.” User’s guide, Ver. 2, Math Works, Inc., Natick, Mass.
Miskawi, Z. (1989). “An S-curve equation for project control.” Constr. Manage. Econom., 7(2), 115–124.
Navon, R. (1996). “Cash flow forecasting and updating for building projects.” Proj. Manage. J., 27(2), 14–23.
Peer, S. (1982). “Application of cost-flow forecasting models.” J. Constr. Div., 108(2), 226–232.
Skitmore, M. (1992). “Parameter prediction for cash flow forecasting models.” Constr. Manage. Econom., 10(5), 397–413.
Tucker, S. N. (1988). “A single alternative formula for Department of Health and Social Security S-curves.” Constr. Manage. Econom., 6(1), 13–23.
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© 2009 ASCE.
History
Received: Sep 18, 2007
Accepted: Sep 11, 2008
Published online: Mar 1, 2009
Published in print: Mar 2009
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