All Title Author
Keywords Abstract


Damage Location Index of Shear Structures Based on Changes in First Two Natural Frequencies

DOI: 10.1155/2014/520310

Full-Text   Cite this paper   Add to My Lib

Abstract:

A method of detecting the location of damage in shear structures by using only the changes in first two natural frequencies of the translational modes is proposed. This damage detection method can determine the damage location in a shear building by using a Damage Location Index (DLI) based on two natural frequencies for undamaged and damaged states. In this study, damage is assumed to be represented by the reduction in stiffness. This stiffness reduction results in a change in natural frequencies. The uncertainty associated with system identification methods for obtaining natural frequencies is also carefully considered. Some simulations and experiments on shear structures were conducted to verify the performance of the proposed method. 1. Introduction In the structural health monitoring (SHM) field, many damage detection algorithms based on the modal properties of a structure, such as modal frequencies, mode shapes, curvature mode shapes, and modal flexibilities, have been studied for several decades. However, with most algorithms, identifying the precise location and magnitude of the damage is difficult. If not completely impossible, the accuracy and reliability are not sufficient [1]. Zhao and DeWolf [2] presented a sensitivity study comparing the use of natural frequencies, mode shapes, and modal flexibilities for monitoring. Based on the fact that natural frequencies are sensitive indicators of structural integrity, the relationship between frequency changes and structural damage was discussed in a review by Salawu [3]. Many of the methods using changes in natural frequencies to detect damage were summarized by Doebling in [4]. The amount of literature is large, comprising not only applications to various structures, but also theoretical work on the use of frequency shifts for damage detection. Besides, the trade-off relation between the number of sensors and the damage detection accuracy should be considered when installing the SHM system. A large number of sensors results in a high system cost as well as the need for enormous effort for wiring and installation. Complicated and expensive SHM systems are not feasible for most buildings [5]. Some methods have performed well at frequency-based damage identification for a few degrees of freedom. For larger engineering structures, the number of natural frequencies that can be identified is smaller than the number of structural elements. This is one of the reasons why frequency change methods have limited damage detection abilities [6]. To overcome these problems, some researchers have been using the

References

[1]  A. Mita, Structural Dynamic for Health Monitoring, Sankeisha, Nagoya, Japan, 2003.
[2]  J. Zhao and J. T. DeWolf, “Sensitivity study for vibrational parameters used in damage detection,” Journal of Structural Engineering, vol. 125, no. 4, pp. 410–416, 1999.
[3]  O. S. Salawu, “Detection of structural damage through changes in frequency: a review,” Engineering Structures, vol. 19, no. 9, pp. 718–723, 1997.
[4]  S. Doebling, Damage Identification and Health Monitoring of Structural and Mechanical Systems from Changes in Their Vibration Characteristics: A Literature Review, Los Alamos National Laboratory, Los Alamos, NM, USA, 1996.
[5]  A. Mita and H. Hagiwara, “Quantitative damage diagnosis of Shear structures using Support Vector Machine,” KSCE Journal of Civil Engineering, vol. 7, no. 6, pp. 683–689, 2003.
[6]  N. Stubbs, T. H. Broome, and R. Osegueda, “Nondestructive construction error detection in large space structures,” AIAA Journal, vol. 28, no. 1, pp. 146–152, 1990.
[7]  Z. Xing and A. Mita, “A substructure approach to local damage detection of shear structure,” Structural Control and Health Monitoring, vol. 19, no. 2, pp. 309–318, 2012.
[8]  J. Sidhu and D. J. Ewins, “Correlation of finite and model test studies of a practical structure,” in Proceeding of the 2nd International Modal Analysis Conference, pp. 756–762, Society for Experimental Mechanics, Orlando, Fla, USA, February 1984.
[9]  H. P. Zhu and Y. L. Xu, “Damage detection of mono-coupled periodic structures based on sensitivity analysis of modal parameters,” Journal of Sound and Vibration, vol. 285, no. 1-2, pp. 365–390, 2005.
[10]  A. Messina, E. J. Williams, and T. Contursi, “Structural damage detection by a sensitivity and statistical-based method,” Journal of Sound and Vibration, vol. 216, no. 5, pp. 791–808, 1998.
[11]  E. P. Carden and A. Mita, “Challenges in developing confidence intervals on modal parameters estimated for large civil infrastructure with stochastic subspace identification,” Structural Control and Health Monitoring, vol. 18, no. 1, pp. 53–78, 2011.
[12]  W. Gao, “Interval natural frequency and mode shape analysis for truss structures with interval parameters,” Finite Elements in Analysis and Design, vol. 42, no. 6, pp. 471–477, 2006.
[13]  W. Gao, “Natural frequency and mode shape analysis of structures with uncertainty,” Mechanical Systems and Signal Processing, vol. 21, no. 1, pp. 24–39, 2007.
[14]  P. Moser and B. Moaveni, “Environmental effects on the identified natural frequencies of the Dowling Hall Footbridge,” Mechanical Systems and Signal Processing, vol. 25, no. 7, pp. 2336–2357, 2011.
[15]  K. He and W. D. Zhu, “Structural damage detection using changes in natural frequencies: theory and applications,” Journal of Physics: Conference Series, vol. 305, no. 1, Article ID 012054, 2011.
[16]  D. Muria-Vila, L. Fuentes, and R. Gonzalez, “Uncertainties in the estimation of natural frequencies of vibration in buildings of Mexico City,” Informacion Tecnologica, vol. 11, no. 3, pp. 177–184, 2000.
[17]  J. F. Clinton, S. C. Bradford, T. H. Heaton, and J. Favela, “The observed wander of the natural frequencies in a structure,” Bulletin of the Seismological Society of America, vol. 96, no. 1, pp. 237–257, 2006.
[18]  V. Lindberg, “Uncertainties and error propagation,” 2008, http://www.rit.edu/cos/uphysics/uncertainties/Uncertaintiespart2.html.
[19]  M. Verhaegen and P. Dewilde, “Subspace model identification part 1: the output-error state-space model identification class of algorithms,” International Journal of Control, vol. 56, no. 5, pp. 1187–1210, 1992.

Full-Text

comments powered by Disqus

Contact Us

service@oalib.com

QQ:3279437679

微信:OALib Journal