Object-Oriented Evolutionary Fuzzy Neural Inference System for Construction Management
Publication: Journal of Construction Engineering and Management
Volume 129, Issue 4
Abstract
Problems in construction management are complex, full of uncertainty, and vary with environment. Fuzzy logic, neural networks, and genetic algorithms (GAs) have been successfully applied in construction management to solve various kinds of problems. Considering the characteristics and merits of each method, this paper combines the above three techniques to develop an Evolutionary Fuzzy Neural Inference Model (EFNIM). Integrating these three methods, the EFNIM uses GAs to simultaneously search for the fittest membership functions with the minimum fuzzy neural network (FNN) structure and optimum parameters of FNN. Thus, the best adaptation mode is automatically identified. Furthermore, this research work integrates the EFNIM with an object-oriented (OO) computer technique to develop an OO Evolutionary Fuzzy Neural Inference System for solving construction management problems. Simulations are conducted to demonstrate the application potential of the EFNIS. This system could be used as a multifarious intelligent decision support system for decision-making to solve manifold construction management problems.
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References
Booch, G. (1994). Object-oriented analysis and design with applications, 2nd Ed., Benjamin, Redwood City, Calif.
Bush, V. G. (1973). Construction management: A handbook for contractors, architects, and students, Reston, Va., 1–6.
D’Souza, D. F., and Wills, A. C. (1999). Objects, components, and frameworks with UML: the catalysis approach, Addison-Wesley, Reading, Mass.
Edwards, D. J., Holt, G. D., and Harris, F. C.(2000). “Estimating life cycle plant maintenance costs.” Constr. Manage. Econom., 18(4), 427–435.
Gen, M., and Cheng, R. (1997). Genetic algorithms and engineering design, Wiley, New York.
Ghezelayagh, H., and Lee, K. Y.(1999). “Training neuro-fuzzy boiler identifier with genetic algorithm and error back-propagation.” Proc., Power Engineering Society Summer Meeting, Vol. 2, IEEE, Edmonton, Canada, 978–982.
Gorzalczany, M. B., and Gradzki, P.(2000). “A neuro-fuzzy-genetic classifier for technical applications.” Proc., IEEE Int. Conf. on Industrial Technology, Vol. 1, IEEE, Goa, India, 503–508.
Hayashi, I., Umano, M., Maeda, T., Bastian, A., and Jain, L. C.(1998). “Acquisition of fuzzy knowledge by NN and GA—A survey of the fusion and union methods proposed in Japan.” Proc., 1998 2nd Int. Conf. on Knowledge-Based Intelligent Electronic Systems, Vol. 1, IEEE, Adelaide, Australia, 69–78.
Haykin, S. (1999). Neural networks: A comprehensive foundation, 2nd Ed., Prentice-Hall, New York.
Holland, J. H. (1975). Adaptation in neural and artificial systems, The Univ. of Michigan Press, Ann Arbor, Mich.
Ishigami, H., Fukuda, T., Shibata, T., and Arai, F.(1995). “Structure optimization of fuzzy neural network by genetic algorithm.” Fuzzy Sets Syst., 71(3), 257–264.
Jacobson, I. (2000). The road to the unified software development process, Cambridge University Press, Cambridge, U.K., 103–108.
Jagielska, I., Matthews, C., and Whitfort, T.(1999). “An investigation into the application of neural networks, fuzzy logic, genetic algorithms, and rough sets to automated knowledge acquisition for classification problems.” Neurocomputing, 24, 37–54.
Larman, C. (1998). Applying UML and patterns: An introduction to object-oriented analysis and design, Prentice Hall PTR, Upper Saddle River, N.J.
Li, H.(1996). “Case-based reasoning for intelligent support of construction negotiation.” Inf. Manage., 30(5), 231–238.
Liatsis, P., and Goulermas, Y. J. P. (1995). “Minimal optimal topologies for invariant higher-order neural architectures using genetic algorithms.” Proc., IEEE Int. Symp. on Industrial Electronics, IEEE, Vol. 2, Athens, Greece, 792–797.
Linkens, D. A., and Nyongesa, H. O.(1996). “Learning systems in intelligent control: An appraisal of fuzzy, neural and genetic algorithm control applications.” IEE Proc.: Control Theory Appl., 143(4), 367–386.
Liska, J., and Melsheimer, S. S. (1994). “Complete design of fuzzy logic systems using genetic algorithms.” Proc., 3rd IEEE Conf. on Fuzzy Systems, Vol. 2, Orlando, Fla., 1377–1382.
Lorterapong, P., and Moselhi, O.(1996). “Project-network analysis using fuzzy sets theory.” J. Constr. Eng. Manage., 122(4), 308–318.
Maier, H. R., Sayed, T., and Lence, B. J.(2000). “Forecasting cyanobacterial concentrations using b-spline networks.” J. Comput. Civ. Eng., 14(3), 183–189.
Mareels, I., and Polderman, J. W. (1996). Adaptive systems: An introduction, Birkhauser, Boston, 1–3.
Martin, N. M., and Jain, L. C. (1999). “Introduction to neural networks, fuzzy systems, genetic algorithms, and their fusion.” Fusion of neural networks, fuzzy sets, and genetic algorithms: industrial applications, L. C. Jain and N. M. Martain, eds., CRC Press, Boca Raton, Fla., 3–12.
Michalewicz, Z. (1996). Genetic algorithms+data structures=evolution programs, 3rd Ed., Springer, New York.
Minsky, M., and Papert, S. (1969). Perceptrons: An introduction to computational geometry, MIT Press, Cambridge, Mass.
Qin, K., Wang, W., and Gong, M. (1997). “A genetic algorithm for the minimum weight triangulation.” IEEE Int. Conf. on Evolutionary Computation, IEEE, Indianapolis, Ind., 541–546.
Satzinger, J. W., Jackson, R. B., and Burd, S. D. (2000). System analysis and design in a changing world, Course Technology, Cambridge, Mass., 67–75.
Tommelein, I. D., Levitt, R. E., and Hayes-Roth, B.(1992). “Site-layout modeling: How can artificial intelligence help?” J. Constr. Eng. Manage., 118(3), 594–611.
Yang, J. B., and Yau, N. J.(2000). “Integrating case-based reasoning and expert system techniques for solving experience-oriented problems.” J. Chin. Inst. Eng., 23(1), 83–95.
Zadeh, L. A.(1965). “Fuzzy sets.” Inf. Control, 8(3), 338–353.
Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 129 • Issue 4 • August 2003
Pages: 461 - 469
Copyright
Copyright © 2003 American Society of Civil Engineers.
History
Received: Feb 5, 2002
Accepted: Jul 1, 2002
Published online: Jul 15, 2003
Published in print: Aug 2003
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