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Construction project control using artificial neural networks

Published online by Cambridge University Press:  27 February 2009

H. Al-Tabtabai ,
N. Kartam ,
I. Flood  and
A.P. Alex
H. Al-Tabtabai
Affiliation:
Civil Engineering Department, College of Engineering and Petroleum, Kuwait University, P.O. Box 5969, Safat 13060, Kuwait.
N. Kartam
Affiliation:
Civil Engineering Department, College of Engineering and Petroleum, Kuwait University, P.O. Box 5969, Safat 13060, Kuwait.
I. Flood
Affiliation:
ME Rinker Sr. School of Building Construction, Gainesville, Florida 32611, USA
A.P. Alex
Affiliation:
Civil Engineering Department, College of Engineering and Petroleum, Kuwait University, P.O. Box 5969, Safat 13060, Kuwait.
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Abstract

Artificial neural networks are finding wide application to a variety of problems in civil engineering. This paper describes how artificial neural networks can be applied in the area of construction project control. A project control system capable of predicting and monitoring project performance (e.g., cost variance and schedule variance) based on observations made from the project environment is described. This project control system has five neural network modules that allow a project manager to automatically generate revised project plans at regular intervals during the progress of the project. These five modules are similar in design and implementation. Therefore, this paper will present the main issues involved in the development of one of these five neural network modules, that is, the module for identifying schedule variance. A description of a graphical user interface integrating the neural network modules developed with project management software, and a discussion on the power and limitations of the overall system conclude the paper.

Keywords

Construction Project ControlArtificial Neural NetworksSchedule ControlCost Control
Type
Articles
Information
AI EDAM , Volume 11 , Issue 1 , January 1997 , pp. 45 - 57
Copyright
Copyright © Cambridge University Press 1997

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References

REFERENCES

Adrian, J. (1987). Construction Productivity Improvement. Elsevier Science Publishing Co., Inc., New York.Google Scholar
Al-Tabtabai, H., & Diekmann, J. (1990). PROCON: A knowledge based approach to construction project control. Proc. CIB, Sydney, Australia, 385–397.Google Scholar
Al-Tabtabai, H., & Diekmann, J. (1992). Judgmental forecasting in construction projects. Constr. Mgmt. Econ., 10, 19–30.CrossRefGoogle Scholar
Al-Tabtabai, H., Kartam, N., Flood, I., & Alex, A.P. (1997). Expert judgment in forecasting construction project completion. In Engineering Construction and Architectural Management. Blackwell Science Ltd., Oxford, UK (Forthcoming).Google Scholar
ASCE (1997). Artificial Neural Networks for Civil Engineers: Fundamentals and Applications (Kartam, N., Flood, I., and Garrett, J., Eds.) ASCE, New York.Google Scholar
Carpenter, W.C., & Hoffman, M.E. (1995). Training backprop neural networks. AI Expert. March, 30–33.Google Scholar
Caudill, M. (1989). Neural networks primer. Al Expert Feb., 61–67.Google Scholar
Cleland, D.I. (1985). A strategy for on-going project evaluation. Proj. Mgmt. J. (Special Summer Issue) Aug., 11–17.Google Scholar
Flood, I., & Kartam, N. (1994). Neural networks in civil engineering: Principles and understanding. J. Comput. Civ. Engrg. 8(2), 131–148.CrossRefGoogle Scholar
Kartam, N.A., & Levitt, R.E. (1990). Intelligent planning of construction projects. J. Comput. Civ. Engrg. 4(2), 155–176.CrossRefGoogle Scholar
Kartam, N., & Levitt, R. (1991). An artificial intelligence approach to project planning under uncertainty. Proj. Mgmt. J. XXII(2), 7–11.Google Scholar
Keisk, T, & Selby, K. (1990). Automating construction estimating. Proc. Annual Conf. and 1st Biennial Environmental Specialty Conference, CSCE, Hamilton, Ontario, Canada, Vol. II-1, 150–160.Google Scholar
Levitt, R.E., & Kunz, J.C. (1985). Using knowledge of construction and project management for automated schedule updating. Proj. Mgmt. J. 16(5), 57–76.Google Scholar
Lippmann, R.P. (1987). An introduction to computing with neural nets. IEEE ASSP Mag. Apr., 4–22.CrossRefGoogle Scholar
Meuller, F. (1986). Integrated Cost and Schedule Control for Construction Projects. Van Nostrand Reinhold Company, New York.CrossRefGoogle Scholar
Moselhi, O., Hegazy, T., & Fazio, P. (1991). Neural networks as tools in construction. J. Constr. Engrg. Mgmt. 117(4), 606–625.CrossRefGoogle Scholar
Neil, J. (1982). Construction Cost Estimating for Project Control. Prentice-Hall Inc., New York.Google Scholar
Riggs, L. (1990). Project control technique. Proc, CIB, Sydney, Australia, 11–25.Google Scholar
Rumelhart, D.E., Hinton, G.E., & Williams, R.J. (1986). Learning internal representation by error propagation. In Parallel Distributed Processing (Rumelhart, D.E., McClelland, J.L., and the PDP Research group, Eds.), pp. 318–362. MIT Press, Cambridge, MA.CrossRefGoogle Scholar
Scheaffer, R.L., & McClave, J.J. (1990). Probability and Statistics for Engineers, 3rd Ed.PWS-KENT Publishing Co., Boston, MA.Google Scholar
Simon, H. (1991). Artificial intelligence: Where has it been, and where is it going? IEEE Trans. Knowledge and Data Engrg. 3(2), 128–136.CrossRefGoogle Scholar
Twomey, J.M., & Smith, A.E. (1997). Artificial Neural Networks for Civil Engineers: Fundamentals and Applications (Kartam, N., Flood, I., and Garrett, J., Eds.). ASCE Press, New York (Forthcoming).Google Scholar
US DOE (1986). Cost/Schedule System Criteria for Contract Performance Measurement—Data Analysis Guide, USA.Google Scholar
Warszawaski, A. (1985). Decision models and expert systems in construction management. Build. Environ. 20(4), 201–210.CrossRefGoogle Scholar
Woolery, J.C., & Crandall, K.C. (1983). Stochastic network model for planning and scheduling. J. Constr. Engrg. Mgmt. 109(3), 342–354.CrossRefGoogle Scholar