Neural Networks for Estimating the Productivity of Concreting Activities
Publication: Journal of Construction Engineering and Management
Volume 132, Issue 6
Abstract
To overcome the variability and the impact of subjective factors on the cost of concrete-related activities in developing countries, neural networks can offer a guiding tool. In this study, three neural networks were developed to estimate the productivity, within a developing market, for formwork assembly, steel fixing, and concrete pouring activities. Eighteen experts working in six projects were carefully selected to gather the data for the neural networks. Ninety-two data surveys were obtained and processed for use by the neural networks. Commercial software was used to perform the neural network calculations. The processed data were used to develop, train, and test the neural networks. The results of the developed framework of neural networks indicate adequate convergence and relatively strong generalization capabilities. When used to perform a sensitivity analysis on the input factors influencing the productivity of concreting activities, the framework has demonstrated a good potential in identifying trends of such factors.
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References
Alsugair, A. M., and Al-Qudrah, A. A. (1998). “Artificial neural network approach for pavement maintenance.” J. Comput. Civ. Eng., 12(4), 249–255.
Arditi, D., and Tokdemir, O. B. (1999). “Comparison of case-based reasoning and artificial neural networks.” J. Comput. Civ. Eng., 13(3), 162–169.
Chao, L., and Skibniewski, J. (1994). “Estimating construction productivity: Neural-network-based approach.” J. Comput. Civ. Eng., 8(2), 234–251.
Ersoz, H. (1999). “Discussion of neural network model for estimating construction productivity.” J. Constr. Eng. Manage., 125(3), 211–212.
Gould, F., Managing the construction process: Estimating, scheduling, and project control, 2nd Ed., Prentice–Hall, Upper Saddle River, N.J.
Halligan, D. W., Demsetz, L. A., Brown, J. D., and Pace, C. B. (1994). “Action-response model and loss of productivity in construction.” J. Constr. Eng. Manage., 120(1), 47–64.
Harvey, R. L. (1994). Neural network principles, Prentice–Hall, Englewood Cliffs, N.J.
Hegazy, T., and Ayed, A. (1998). “Neural network model for parametric cost estimation of highway projects.” J. Constr. Eng. Manage., 124(3), 210–218.
Li, H., Chen, L. Y., and Love, P. E. D. (1999). “ANN-based mark-up estimation system with self-explanatory capacities.” J. Constr. Eng. Manage., 125(3), 185–189.
NeuroSolutions 4 (2001), NeuroDimensions Inc., Gainesville, Fl.
Portas, J., and Abourizk, S. (1997). “Neural network model for estimating construction productivity.” J. Constr. Eng. Manage., 123(4), 399–410.
Sonmez, R., and Rowings, J. E. (1998). “Construction labor productivity modeling with neural networks.” J. Constr. Eng. Manage., 124(6), 498–504.
Thomas, R. H., Riley, D. R., and Sanvido, V. E. (1999). “Loss of labor productivity due to delivery methods and weather.” J. Constr. Eng. Manage., 125, 39–46.
Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 132 • Issue 6 • June 2006
Pages: 650 - 656
Copyright
© 2006 ASCE.
History
Received: May 2, 2005
Accepted: Oct 19, 2005
Published online: Jun 1, 2006
Published in print: Jun 2006
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