Neural Networks as Tools in Construction
Publication: Journal of Construction Engineering and Management
Volume 117, Issue 4
Abstract
The competitive and risk‐averse nature of the construction industry and its heuristic problem‐solving needs, among other reasons, have contributed to the development of nontraditional decision‐making tools. Research in artificial intelligence (AI), a branch of computer science, has provided more suitable tools to the construction industry. Expert systems have steadily been introduced for different applications in the industry. However, the performance of these systems during the last decade, is far from ideal. Neural networks research in AI has recently provided powerful systems that work as a supplement or a complement to such conventional expert systems. In this paper, neural networks are introduced as a promising management tool that can enhance current automation efforts in the construction industry, including expert systems applications. Basic neural network architectures are described, and their potential applications in construction engineering and management discussed. A neural network application is developed for optimum markup estimation. Future possibilities of integrating neural networks and expert systems as a basis for developing efficient intelligent systems are described.
Get full access to this article
View all available purchase options and get full access to this article.
References
1.
Adeli, H., ed., (1988). Expert systems in construction and structural engineering. Chapman and Hall, Ltd., London, England.
2.
Ahmad, I., and Minkarah, I. (1988). “Questionnaire survey on bidding in construction.” J. Mgmt. Engrg., ASCE, 4(3), 229–243.
3.
Ahmad, I., and Minkarah, I. (1990). “Decision analysis and expert system technology: A construction industry perspectcive.” Proc., Int. Symp. on Building Economics and Construction Management, CIB, Sydney, Australia, 363–372.
4.
Allwood, R. J. (1989). Techniques and application of expert systems in construction industry. Ellis Horwood Ltd., Chichester, England.
5.
Bailey, D., and Thompson, D. (1990). “How to develop neural‐network applications.” AI Expert, 38–47.
6.
Bowen, P. A., and Erwin, G. J. (1990). “The impact of second generation expert systems on building design evaluation.” Proc., Int. Symp. on Building Economics and Construction Management, CIB, Sydney, Australia, 419–427.
7.
Brandon, P. S. (1990). “Expert systems—After the hype is over.” Proc., Int. Symp. on Building Economics and Constructcion Management, CIB, Sydney, Australia, 314–346.
8.
Carr, R. (1987). “Optimum markup by direct solution.” J. Constr. Engrg. Mgmt., ASCE, 113(1), 138–150.
9.
Carpenter, G., and Grossberg, S. (1987a). “A massively parallel architecture for a self‐organizing neural pattern recognition machine,” Computer Vision, Graphics, and Image Processing, 37, 54–115.
10.
Carpenter, G., and Grossberg, S. (1987b). “ART‐2: Self‐organization of stable category recognition codes for analog input patterns,” Appl. Optics, 26(23), 4919–4930.
11.
Castelaz, P., Angus, J., and Mahoney, J. (1987). “Application of neural networks to expert systems and command and control systems.” Proc., IEEE Western Conf. on Expert Systems, 118–125.
12.
Cohen, M. A., and Grossberg, S. G. (1983). “Absolute stability of global pattern formation and parallel memory storage by competitive neural networks.” IEEE Trans. Syst. Man Cybern., 13, 815–826.
13.
Derthick, M. (1987). “A connectionist architecture for representing and reasoning about structured knowledge.” Proc., Ninth Annual Conf. Cognitive Science Soc.
14.
Fahlman, S. E., and Hinton, G. E. (1986). “Connectionist architectures for artificial intelligence.” Computer, 19, 100–108.
15.
Flood, I. (1989). “A neural network approach to the sequencing of construction tasks.” Proc., 6th Int. Symp. on Automation and Robotics in Construction, ISARC, 204–211.
16.
Flood, I. (1990). “Solving construction operational problems using artificial neural networks and simulated evolution.” Proc., Int. Symp. on Building Economics and Construction Management, CIB, Sydney, Australia, 197–208.
17.
Friedman, L. (1956). “A competitive bidding strategy.” Oper. Res., 4, 104–112.
18.
Fung Fai, N. G. (1990). “Knowledge base systems: Their impact on the construction industry.” Proc., Int. Symp. on Building Economics and Construction Management, CIB, Sydney, Australia, 549–560.
19.
Gallant, S. (1988). “Connectionist expert systems.” Commun. ACM, 31(2), 152–169.
20.
Gates, M. (1967). “Bidding strategies and probabilities.” J. Constr. Div., ASCE, 93(1), 75–103.
21.
Grossberg, S. (1974). “Classical and instrumental learning by neural networks.” Progress in theoretical biology, Academic Press, NY, 3, 51–141.
22.
Grossberg, S. (1982). Studies of mind and brain. Reidel, Boston, Mass.
23.
Hebb, D. O. (1949). Organization of behaviour. Science Editions, New York, N.Y.
24.
Hecht‐Nielsen, R. (1987). “Counterpropagation networks.” Appl. Optics, 26(23), 979–984.
25.
Hecht‐Nielsen, R. (1988). “Applications of counterpropagation networks.” Neural Networks, 1(2), 131–139.
26.
Hinton, G. E., and Sejnowski, T. J. (1986). “Learning and relearning in Boltzmann machines.” Parallel distributed processing, 1, MIT Press, Cambridge, Mass., 282–317.
27.
Hopfield, J. J. (1982). “Neural networks and physical systems with emergent collective computational abilities.” Proc., Nat. Acad. Sci., 79, 2554–2558.
28.
Hopfield, J. J. (1984). “Neurons with graded response have collective computational properties like those of two‐state neurons.” Proc., Nat. Acad. Sci., 81, 3088–3092.
29.
Hopfield, J. J. (1987). “Learning algorithms and probability distributions in feedforward and feed‐back networks.” Proc., Nat. Acad. Sci., 74, 8429–8433.
30.
“An introduction to neural computing.” (1988). Neural networks user guide, Neural Ware, Sewickley, PA.
31.
Jackson, P. (1986). Introduction to expert systems. Addison‐Wesley, London, U.K.
32.
Kohonen, T. (1984). “Self‐organization and associative memory.” Series in information sciences, 8, Springer Verlag, Berlin, Germany.
33.
Kohonen, T. (1988). Self‐organization and associative memory. 2nd Ed., Springer‐Verlag, New York, N.Y.
34.
Kosko, B. (1987a). “Bi‐directional associative memories.” IEEE Trans. Syst. Man Cybern., 18(1), 49–60.
35.
Kosko, B. (1987b). “Competitive adaptive bi‐directional associative memories.” Proc., IEEE First Int. Conf. on Neural Networks, 2, SOS Printing, San Diego, Calif., 759–766.
36.
Levitt, R. E. (1987). “Expert systems in construction: State of the art.” Expert systems for civil engineers: Technology and applications, M. L. Maher, ed., ASCE, New York, N.Y., 85–112.
37.
Minkarah, I., and Ahmad, I. (1989). “Expert systems as construction management tools.” J. Mgmt. Engrg., ASCE, 5(2), 155–163.
38.
Minsky, M. I., and Papert, S. (1969). Perceptrons. MIT Press, Cambridge, Mass.
39.
Mohan, S. (1990). “Expert systems applications in construction management and engineering.” J. Constr. Engrg. Mgmt., ASCE, 116(1), 87–99.
40.
Moselhi, O., and Hegazy, T. (1990a). “Optimum makeup estimation: A comparative study.” Proc., 11th Int. Cost Engineering Congr., 6th AFITEP Annual Meeting, M.2.1–M.2.8.
41.
Moselhi, O., and Hegazy, T. (1990b). “Bidding for a profit: A hockey stick trend.” Proc., Project Management Institute, 1990 Annual Seminar/Symposium, 435–442.
42.
Pao, Y. H. (1988). “Autonomous machine learning of effective control strategies with connectionist‐nets.” J. Intelligent Robotic Syst., 1, 35–53.
43.
Pao, Y. H. (1989). Adaptive pattern recognition and neural networks. Addison‐Wesley Publishing Co., Reading, Mass.
44.
Pao, Y. H., and Sobajic, D. J. (1988). “Autonomous features discovery for critical clearing time assessment.” Proc., Expert System Applications to Power Systems, Stockholm, Sweden.
45.
Rosenblatt, F. (1959). Principles of neorodynamics. Spartan Books, New York, N.Y.
46.
Rosenblatt, F. (1961). Principles of neorodynamics: Perceptrons and the theory of brain mechanisms. Spartan Books, Washington D.C.
47.
Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1988). “Learning internal representations by error propagation.” Parallel distributed processing: Explorations in the microstructures of cognition. Vol. 1: Foundation, D. E. Rumelhart and J. L. McClelland, eds., MIT Press, Cambridge, Mass.
48.
Sejnowski, T. J. (1986). “Higher order Boltzmann machines.” American Institute of Physics. Conf. Proc. No. 151, Neural Networks for Computing, Snowbird, Utah, 398–403.
49.
Sejnowski, T. J., and Rosenberg, C. R. (1987). “Parallel networks that learn to pronounce English text.” Complex Syst., 1, 145–168.
50.
Stubbs, D. (1990). “Products to stimulate and simulate neuroComputing Rap.” AI Expert, 61–63.
51.
Wassermann, P. D. (1989). Neural computing: Theory and practice. Van Nostrand Reinhold, New York, N.Y.
52.
Wassermann, P. D. (1988). “Combined backpropagation/Cauchy machine.” Neural Networks, Abstracts of the First INNS Meeting, 1, Pergamon Press, Elmsford, N.Y.
Information & Authors
Information
Published In
Copyright
Copyright © 1991 ASCE.
History
Published online: Dec 1, 1991
Published in print: Dec 1991
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.