Rank Correlations In Simulating Construction Costs
Publication: Journal of Construction Engineering and Management
Volume 123, Issue 3
Abstract
In this paper the use of rank correlations in simulating construction costs is investigated. One suggested methodology for generating correlated random numbers using rank correlations is reviewed and compared with traditional methods based on Pearson correlations. This methodology is the basis for the design of several simulation software packages commonly used by analysts and estimators. Because of this it is important to evaluate the effectiveness of this approach in probabilistic analysis of construction costs. A set of real-life construction costs is used to test the effectiveness of the suggested methodology in simulating the distribution of costs. Several tests of hypotheses are executed to compare the distribution of simulated data with actual data. It is shown that rank correlations can model data dependency as effectively as Pearson correlations on this data set. Suggestions are made regarding future work in this area.
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References
1.
AbouRizk, S. M., and Halpin, D. W.(1990). “Probabilistic simulation studies for repetitive construction processes.”J. Constr. Engrg. and Mgmt., ASCE, 116(4), 575–594.
2.
Crystal Ball user's manual. (1993). Decisioneering, Inc., Denver, Colo.
3.
Curran, M. W.(1989). “Range estimating.”Cost Engrg., 31(3), 18–26.
4.
Devore, J. L. (1991). Probability and statistics for engineering and sciences, 3rd Ed., Brooks/Cole Publishing Co., Pacific Grove, Calif.
5.
Diekmann, J. E.(1983). “Probabilistic estimating: mathematics and applications.”J. Constr. Engrg. and Mgmt., ASCE, 109(3), 297–308.
6.
Fishman, G. S. (1978). Principles of discrete event simulation. John Wiley & Sons, Inc., New York, N.Y.
7.
Iman, R. L., and Conover, W. J.(1982). “A distribution-free approach to inducing rank correlations among input variables.”Communications in Statistics, 11(3), 311–334.
8.
Iman, R. L., and Davenport, J. M.(1982). “Rank correlation plots for use with correlated input variables.”Communications in Statistics, 11(3), 335–360.
9.
Johnson, M. E., and Ramberg, J. S (1978). “Transformations of the multivariate normal distribution with applications to simulation.”Tech. Rep. LA-UR-77-2595, Los Alamos Sci. Lab., Los Alamos, N.M.
10.
Law, A. M., and Kelton, W. D (1991). Simulation modeling and analysis, 2nd Ed., McGraw-Hill, New York, N.Y.
11.
Siegel, S., and Castellan Jr., N. J. (1988). Nonparametric statistics for the behavioral sciences, 2nd Ed., McGraw-Hill, New York, N.Y.
12.
Suphot, L. (1994). “Probabilistic estimating for construction costs by using rank correlations,” MS thesis, Northeastern Univ., Boston, Mass.
13.
Touran, A.(1992). “Risk modeling and measurement in construction.”Civ. Engrg. Pract., 7(1), 29–46.
14.
Touran, A., and Wiser, E. P.(1992). “Monte Carlo technique with correlated random variables.”J. Constr. Engrg. and Mgmt., ASCE, 118(2), 258–272.
15.
Wiser, E. P. (1991). “Analysis of cost data in commercial construction projects,” MS thesis, Northeastern Univ., Boston, Mass.
Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 123 • Issue 3 • September 1997
Pages: 297 - 301
Copyright
Copyright © 1997 American Society of Civil Engineers.
History
Published online: Sep 1, 1997
Published in print: Sep 1997
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