Improving Knowledge of Structural System Behavior through Multiple Models
Publication: Journal of Structural Engineering
Volume 134, Issue 4
Abstract
A system identification and model updating methodology that accounts for factors influencing the reliability of identification is proposed. An important aspect of this methodology is the generation of a population of candidate models. This paper presents an analysis of error sources that are used to define model populations. A case study illustrates the need for such an approach even when a single conservative model has been appropriate for design. Data mining techniques such as principal component analysis and -means clustering combined to interpret model predictions. These methods are useful for estimating the dependability of system identification.
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Acknowledgments
This work is part of the current results of several years of funding by the Swiss National Science Foundation and the Commission for Technology and Innovation. The writers would like to thank B. Raphael for his support, and E. Bruehwiler, P. Kripakaran, S. Ravindran, and A. Salvo for their assistance with the Schwandbach Bridge case study.
References
Alonso, C., Rodriguez, J., and Pulido, B. (2004). “Enhancing consistency based diagnosis with machine learning techniques.” Lect. Notes Comput. Sci., 3040, 312–321.
Billington, D. (1979). Robert Maillart’s bridges: The art of engineering, Princeton University Press, Princeton, N.J.
Chan, Z., Collins, L., and Kasabov, N. (2006). “An efficient greedy -means algorithm for global gene trajectory clustering.” Expert Sys. Applic., 30(1), 137–141.
Chaudhary, M., Abe, M., Fujino, Y., and Yoshida, J. (2000). “Performance evaluation of two base-isolated bridges using seismic data.” J. Struct. Eng., 116(10), 1187–1195.
Ding, C., and He, X. (2004). “ -means clustering via principal component analysis.” Proc., 21st Int. Conf. on Machine Learning, ACM Int. Conf. Proc. Series 29, Association for Computing Machinery, New York.
Falkenhainer, B., and Forbus, K. (1991). “Compositional modeling: Finding the right model for the job.” Artif. Intell., 51, 95–143.
Friswell, M., and Mottershead, J. (1995). Finite-element model updating in structural dynamics, Kluwer Academic, Dordrecht, The Netherlands.
Grossman, R., Kamath, C., Kegelmeyer, P., Kumar, V., and Namburu, R., eds. (2001). Data mining for scientific and engineering applications, Kluwer Academic, Dordrecht, The Netherlands.
Hand, D., Mannila, H., and Smyth, P. (2001). Principles of data mining, MIT, Cambridge, Mass.
Jolliffe, I. (2002). Principal component analysis, Springer, New York.
Lendasse, A., Lee, J., De Bodt, E., Wertz, V., and Verleysen, M. (2001). “Dimension reduction of technical indicators for the prediction of financial time series—Application to the bel20 market index.” Eur. J. Econ. Soc. Syst., 15(2), 31–48.
Lexin, L., and Hongzhe, L. (2004). “Dimension reduction methods for microarrays with application to censored survival data.” Technical Rep., Center for Bioinformatics and Molecular Biostatistics, ⟨http://repositories.cdlib.org/cbmb/surv2⟩.
Liu, P., and Chian, C. (1997). “Parametric identification of truss structures using static strains.” J. Struct. Eng., 123(7), 927–933.
Ljung, L. (1999). System identification—Theory for the user, Prentice-Hall, Englewood Cliffs, N.J.
Nguyen, H., and Chan, C. (1999). “Applications of data analysis techniques for oil production prediction.” Artif. Intell. Eng., 13, 257–272.
Packhama, I., Rafiqb, M., Borthwickb, M., and Denham, S. (2005). “Interactive visualization for decision support and evaluation of robustness in theory and in practice.” Adv. Eng. Inf., 19, 263–280.
Pan, X., Ye, X., and Zhang, S. (2005). “A hybrid method for robust car plate character recognition.” Eng. Applic. Artif. Intell., 18(8), 963–972.
Picone, J. (1990). “Duration in context clustering for speech recognition.” Speech Commun., 9(2), 119–128.
Raphael, B., and Smith, I. F. C. (1998). “Finding the right model for bridge diagnosis.” Lect. Notes Artif. Intell., 1454, 308–319.
Raphael, B., and Smith, I. F. C. (2003a). “A direct stochastic algorithm for global search.” J. Appl. Math. Comput., 146(2-3), 729–758.
Raphael, B., and Smith, I. F. C. (2003b). Fundamentals of computer-aided engineering, Wiley, New York.
Reich, G. W., and Park, K. C. (2001). “A theory for strain-based structural system identification.” J. Appl. Mech., 68(4), 521–527.
Robert-Nicoud, Y. (2003). “Une methodologie mesures—Modeles pour l’identification de systemes de genie civil.” Ph.D. thesis, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland.
Robert-Nicoud, Y., Raphael, B., and Smith, I. (2000). “Decision support through multiple models and probabilistic search.” Proc., Construction Information Technology, 765–779.
Robert-Nicoud, Y., Raphael, B., and Smith, I. F. C. (2004). “Improving the reliability of system identification.” Next Gener. Intell. Syst. Eng., Fortschr.-Ber. VDI, 4(199), 100–109.
Robert-Nicoud, Y., Raphael, B., Burdet, O., and Smith, I. F. C. (2005a). “Model identification of bridges using measurement data.” Comput. Aided Civ. Infrastruct. Eng., 20(2), 118–131.
Robert-Nicoud, Y., Raphael, B., and Smith, I. F. C. (2005b). “Configuration of measurement systems using Shannon’s entropy function.” Comput. Struct., 83(8-9), 599–612.
Robert-Nicoud, Y., Raphael, B., and Smith, I. F. C. (2005c). “System identification through model composition and stochastic search.” J. Comput. Civ. Eng., 19(3), 239–247.
Saitta, S., Raphael, B., and Smith, I. F. C. (2005). “Data mining techniques for improving the reliability of system identification.” Adv. Eng. Inf., 19(4), 289–298.
Salvo, A. (2006). “Ponts de Robert Maillart.” Technical Rep., EPFL-MCS, Lausanne, Switzerland.
Sempere, J., and Lopez, D. (2003). “Learning decision trees and tree automata for a syntactic pattern recognition task.” Pattern Recogn. Image Anal., 943–950.
Shirazi Kia, S., Noroozi, S., Carse, B., and Vinney, J. (2005). “Application of data mining techniques in predicting the behaviour of composite joints.” Proc., 8th Int. Conf. on the Application of Artificial Intelligence to Civil, Structural, and Environmental Engineering (CD-ROM), Civil-Comp, Stirling, U.K.
Smith, I. F. C. (2005). “Sensors, models, and videotape.” Proc., Int. Conf. on Computing in Civil Engineering, ASCE, Reston Va.
Tan, P.-N., Steinbach, M., and Kumar, V. (2006). Introduction to data mining, Addison-Wesley, Boston.
Washio, T. (2007). “Applications eligible for data mining.” Adv. Eng. Inf., 21, 241–242.
Webb, A. (2002). Statistical pattern recognition, Wiley, New York.
Wilking, D., and Roefer, T. (2004). “Realtime object recognition using decision tree learning.” RoboCup, lecture notes in artificial intelligence, Vol. 3276, Springer, New York, 556–563.
Xu, L., Yan, Y., Cornwell, S., and Riley, G. (2005). “Online fuel tracking by combining principal component analysis and neural network techniques.” IEEE Trans. Instrum. Meas., 54(4), 1640–1645.
Yan, L., Fraser, M., Oliver, K., Elgamal, A., Conte, J., and Fountain, T. (2005). “Traffic pattern recognition using an active learning neural network and principal components analysis.” Proc., 8th Int. Conf. on the Application of Artificial Intelligence to Civil, Structural, and Environmental Engineering, Civil-Comp, Stirling, U.K.
Yun, C.-B., Yi, J.-H., and Bahng, E. (2001). “Joint damage assessment of framed structures using a neural networks technique.” Eng. Struct., 23, 425–435.
Zhou, L., Shi, Y., Feng, J., and Sears, A. (2005). “Data mining for detecting errors in dictation speech recognition.” IEEE Trans. Speech Audio Process., 13(5), 681–688.
Information & Authors
Information
Published In
Journal of Structural Engineering
Volume 134 • Issue 4 • April 2008
Pages: 553 - 561
Copyright
© 2008 ASCE.
History
Received: Jul 13, 2006
Accepted: Apr 30, 2007
Published online: Apr 1, 2008
Published in print: Apr 2008
Notes
Note. Associate Editor: Finley A. Charney
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