Model for Construction Budget Performance—Neural Network Approach
Publication: Journal of Construction Engineering and Management
Volume 123, Issue 3
Abstract
A neural network approach is used to identify the key management factors that affect budget performance in a project. Field data of project performance has been used to build the budget performance model. This approach allows the model to be built even if the functional interrelationships between input factors and output performance cannot be clearly defined. Altogether eight key determining factors were identified covering areas related to the project manager, project team, and planning and control efforts, namely: number of organizational levels between project manager and craftsmen, project manager experience on similar technical scope, detailed design complete at start of construction, constructability program, project team turnover rate, frequency of control meetings during construction, frequency of budget updates, and control system budget. The model is able to give good predictions even with previously unseen data and incomplete information on the key factors. The model can be used to evaluate various management strategies and thus resources can be effectively deployed to strengthen these aspects of project management.
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References
1.
Assaf, S. A., Al-Khalil, M., and Al-Hazmi, M.(1995). “Causes of delay in large building construction projects.”J. Mgmt. in Engrg., ASCE, 11(2), 45–50.
2.
Bailey, D., and Thompson, D.(1990). “How to develop neural-network application.”AI Expert, 5(6), 38–47.
3.
Boynton, A. C., and Zmud, R. W.(1984). “An assessment of critical success factors.”Sloan Mgmt. Rev., 25(4), 17–27.
4.
Chao, L.-C., and Skibniewski, M. J.(1994). “Estimating construction productivity: neural-network-based approach.”J. Comp. in Civ. Engrg., ASCE, 8(2), 234–251.
5.
Construction Management Committee of the ASCE Construction Division.(1991). “Constructability and constructability programs: white paper.”J. Constr. Engrg. and Mgmt., ASCE, 117(1), 67–89.
6.
Flood, I., and Kartam, N.(1994a). “Neural networks in civil engineering. I: principles and understanding.”J. Comp. in Civ. Engrg., ASCE, 8(2), 131–148.
7.
Flood, I., and Kartam, N.(1994b). “Neural networks in civil engineering. II: systems and application.”J. Comp. in Civ. Engrg., ASCE, 8(2), 149–162.
8.
Fu, L. M. (1994). Neural networks in computer intelligence. McGraw-Hill, Inc., Singapore.
9.
Groak, S.(1994). “Is construction an industry?”Constr. Mgmt. and Economics, 12(4), 287–293.
10.
Jaselskis, E. J. (1988). “Achieving construction project success through predictive discrete choice models,” PhD thesis, Univ. of Texas, at Austin, Tex.
11.
Jaselskis, E. J., and Ashley, D. B.(1991). “Optimal allocation of project management resources for achieving success.”J. Constr. Engrg. and Mgmt., ASCE, 117(2), 321–340.
12.
Kamarthi, S. V., Sanvido, V. E., and Kumara, S. R. T.(1992). “Neuroform-neural network system for vertical formwork selection.”J. Comp. in Civ. Engrg., ASCE, 6(2), 178–199.
13.
Might, R. J., and Fisher, W. A.(1985). “The role of structural factors in determining project management success.”IEEE Trans. on Engrg. Mgmt., 32(2), 71–77.
14.
Moselhi, O., Hegazy, T., and Fazio, P.(1991). “Neural networks as tools in construction.”J. Constr. Engrg. and Mgmt., ASCE, 117(4), 606–625.
15.
Murtaza, M. B., and Fisher, D. J.(1994). “Neuromodex-neural network system for modular construction decision making.”J. Comp. in Civ. Engrg., ASCE, 8(2), 221–233.
16.
Murtaza, M. B., Fisher, D. J., and Musgrove, J. G.(1993). “Intelligent cost/schedule estimation for modular construction.”Cost Engrg., 35(6), 19–25.
17.
Nahapiet, J., and Nahapiet, H. (1985). The management of construction projects—case studies from the USA and UK. The Chartered Inst. of Build.
18.
Okpala, D. C., and Aniekwu, A. N.(1988). “Causes of high costs of construction in Nigeria.”J. Constr. Engrg. and Mgmt., ASCE, 114(2), 233–244.
19.
Pinto, J. K., and Covin, J. G.(1989). “Critical factors in project implementation: a comparison of construction and R&D projects.”Technovation, 9(1), 49–62.
20.
Pinto, J. K., and Mantel, S. J.(1990). “The causes of project failure.”IEEE Trans. on Engrg. Mgmt., 37(4), 269–276.
21.
Pinto, J. K., and Slevin, D. P.(1987). “Critical factors in successful project implementation.”IEEE Trans. on Engrg. Mgmt., 34(1), 22–27.
22.
Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986). “Learning internal representations by error propagation.”Parallel Distributed Processing, Vol. 1, D. E. Rumelhart and J. McClelland, eds., MIT Press, Cambridge, Mass.
23.
Sanvido, V., Grober, F., Parfitt, K., Guvenis, M., and Coyle, M.(1992). “Critical success factors for construction projects.”J. Constr. Engrg. and Mgmt., ASCE, 118(1), 94–111.
24.
Sidwell, A. C.(1983). “An evaluation of management contracting.”Constr. Mgmt. and Economics, 1(1), 47–55.
25.
Skapura, D. M. (1996). Building neural networks. ACM New York, New York, N.Y.
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Copyright © 1997 American Society of Civil Engineers.
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Published online: Sep 1, 1997
Published in print: Sep 1997
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