Mechanical Properties and Fuzzy Modeling of High-Damping Rubber with Thermal Effects
Publication: Journal of Materials in Civil Engineering
Volume 19, Issue 5
Abstract
An experimental program is reported in which the mechanical properties of high-damping natural rubber were measured. Rubber specimens were tested using a custom-designed mechanical apparatus that is equipped with a temperature chamber as well as displacement and force transducers. Each specimen was subjected to a wide range of carefully controlled dynamic loading tests. Variables studied include the strain, strain rate, and temperature of the material. Results from laboratory testing were used to formulate a fuzzy model of mechanical behavior for shear of the rubber compound. Instead of a mathematical representation of the high-damping rubber material, a Takagi-Sugeno-Kang fuzzy inference system is used to relate three input variables with the shear stress. Parameters of seven membership functions are determined through the use of an (adaptive neurofuzzy inference system). Experimental data applied for training and checking of the model are concatenated sets. The resulting fuzzy inference system is shown graphically and analytically to represent accurately the behavior of the high-damping rubber while minimizing computational requirements.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This research is supported by a grant from the Chilean National Fund for Science and Technology, FONDECYT, Project No. 1011025. The writers also gratefully acknowledge the support of the University of Chile and Texas A&M University.
References
Chang, K. C., Soong, T. T., Oh, S.-T., and Li, M. L. (1992). “Effect of ambient temperature on viscoelastically damped structure,” J. Struct. Eng., 118(7), 1955–1973.
Chapra, S. C., and Canale, R. P. (1988). Numerical methods for engineers, McGraw-Hill, New York.
Jang, R. J.-S., Sun, C.-T., and Mizutani, E. (1997). Neuro-fuzzy and soft computing: A computational approach to learning and machine intelligence, Prentice-Hall, Upper Saddle River, N.J.
The Math Works, Inc. (2005a). MATLAB users’ guide: Fuzzy logic toolbox, Nantick, Mass.
The Math Works, Inc. (2005b). SIMULINK users’ guide, Natick, Mass.
Roschke, P. N., Thompson, M. F., and Sicking, D. L. (1990). “Selection of rubber materials and shapes for energy-absorbing elements.” J. Mater. Civ. Eng., 2(4), 240–259.
Spencer, B. F., Dyke, S. J., Sain, M. K., and J. D. Carlson, J. D. (1997). “Phenomenological model for magnetorheological dampers.” J. Eng. Mech., 123(3), 230–238.
Sugeno, M., and Kang, G. T. (1988). “Structure identification of fuzzy model.” Fuzzy Sets Syst., 28(1), 15–33.
Suizu, Y., Suzuki, S., Kasahara, Y., Fujita, S., and Fujita, T. (1995). “Research, development and application of high-damping rubber dampers for vibration control of buildings.” Proc., Int. Post-SMIRT Conf. Seminar, Santiago, Chile, 272–286.
Takagi, T., and Sugeno, M. (1985). “Fuzzy identification of systems and its applications to modeling and control.” IEEE Trans. Syst. Man Cybern., 15(1), 116–135.
Yakut, A. (2002). “Parameters influencing performance of elastomeric bearings at low temperatures.” J. Struct. Eng., 128(8), 986–994.
Information & Authors
Information
Published In
Copyright
© 2007 ASCE.
History
Received: Aug 30, 2005
Accepted: Mar 17, 2006
Published online: May 1, 2007
Published in print: May 2007
Notes
Note. Associate Editor: Shin-Che Huang
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.