- About this Journal ·
- Abstracting and Indexing ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Articles in Press ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Table of Contents
International Journal of Distributed Sensor Networks
Volume 2012 (2012), Article ID 798714, 11 pages
Structural Damage Information Fusion Based on Soft Computing
Beijing Laboratory of Earthquake Engineering and Structural Retrofit, Beijing University of Technology, Beijing 100124, China
Received 11 July 2012; Accepted 3 August 2012
Academic Editor: Liguo Zhang
Copyright © 2012 Haoxiang He et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- G. W. Housner, L. A. Bergman, T. K. Caughey et al., “Structural control: past, present, and future,” Journal of Engineering Mechanics, vol. 123, no. 9, pp. 897–971, 1997.
- D. L. Hall, Mathematical Techniques in Multi-Sensor Data Fusion, Artech House, 1992.
- S. F. Jiang, C. Fu, and C. Zhang, “A hybrid data-fusion system using modal data and probabilistic neural network for damage detection,” Advances in Engineering Software, vol. 42, no. 6, pp. 368–374, 2011.
- H. Y. Guo, “Structural damage detection using information fusion technique,” Mechanical Systems and Signal Processing, vol. 20, no. 5, pp. 1173–1188, 2006.
- M. H. O'Brien and M. J. Loughlin, “Displacement damage quantification in future fusion systems,” Fusion Engineering and Design, vol. 82, no. 15–24, pp. 2536–2542, 2007.
- H. Sohn and K. H. Law, “A Bayesian probabilistic approach for structure damage detection,” Earthquake Engineering and Structural Dynamics, vol. 26, no. 12, pp. 1259–1281, 1997.
- J. L. Beck and S. K. Au, “Bayesian updating of structural models and reliability using Markov chain Monte Carlo simulation,” Journal of Engineering Mechanics, vol. 128, no. 4, pp. 380–391, 2002.
- O. Basir and X. Yuan, “Engine fault diagnosis based on multi-sensor information fusion using Dempster-Shafer evidence theory,” Information Fusion, vol. 8, no. 4, pp. 379–386, 2007.
- Q. G. Fei, A. Q. Li, and X. L. Han, “Simulation study on damage localization of a beam using evidence theory,” Procedia Engineering, vol. 1, no. 1, pp. 147–150, 2009.
- A. Smyth and M. Wu, “Multi-rate Kalman filtering for the data fusion of displacement and acceleration response measurements in dynamic system monitoring,” Mechanical Systems and Signal Processing, vol. 21, no. 2, pp. 706–723, 2007.
- T. Boutros and M. Liang, “Mechanical fault detection using fuzzy index fusion,” International Journal of Machine Tools and Manufacture, vol. 47, no. 11, pp. 1702–1714, 2007.
- Y. Y. Liu, Y. F. Ju, C. D. Duan, and X.-F. Zhao, “Structure damage diagnosis using neural network and feature fusion,” Engineering Applications of Artificial Intelligence, vol. 24, no. 1, pp. 87–92, 2011.
- X. Fang, H. Luo, and J. Tang, “Structural damage detection using neural network with learning rate improvement,” Computers and Structures, vol. 83, no. 25-26, pp. 2150–2161, 2005.
- J. Zhang, “Improved on-line process fault diagnosis through information fusion in multiple neural networks,” Computers and Chemical Engineering, vol. 30, no. 3, pp. 558–571, 2006.
- V. N. Vapnik, The Nature of Statistical Learning Theory, Springer Press, 1995.
- H. X. He and W. M. Yan, “Structural damage detection with wavelet support vector machine: introduction and applications,” Structural Control and Health Monitoring, vol. 14, no. 1, pp. 162–176, 2007.
- F. M. Reza, An Introduction to Information Theory, Dover Publications, 1994.
- C. Wen, Matter-Element Model and Application, Science and Technology Literature Press, 1994.