Factors Influencing Natural Frequencies in a Prestressed Concrete Panel for Damage Detection

Authors

  • L. D. Goh Faculty of Civil Engineering, Universiti Teknologi MARA, Malaysia
  • A. A. Rahman Faculty of Civil Engineering, Universiti Teknologi Malaysia, Malaysia
  • N. Bakhary Faculty of Civil Engineering, Universiti Teknologi Malaysia, Malaysia
  • B. H. Ahmad Faculty of Civil Engineering, Universiti Teknologi Malaysia, Malaysia

DOI:

https://doi.org/10.11113/jt.v69.3143

Keywords:

Natural frequency, finite element model, modal test, prestressed concrete panel

Abstract

Modal parameters such as natural frequencies, mode shapes, and damping ratios are widely used as damage indicators in the field of vibration-based damage detection. These modal parameters can be easily obtained by conducting the modal test on the actual structure or from the finite element model. However, many publications are focusing only on the relationship between the modal parameters and the changes in structural properties for damage detection. There are a limited number of publications discussing on the factors that may affect the modal parameters for damage detection. Hence, this paper provides a study on the level of influence of several factors on the natural frequencies of a prestressed concrete panel. The factors that are considered in this study are the size of element used in the numerical model, the dimension of the structural element, and the prestressing force applied in the prestressed concrete panel. The natural frequencies computed from the finite element model are also verified with the actual measured natural frequencies that are determined through the modal test conducted in the laboratory. 

References

S. W. Doebling, C. R. Farrar, M. B. Prime, and D. W. Shevitz. 1996. Damage Identification and Health Monitoring of Structural and Mechanical Systems from Changes in Their Vibration Characteristics: A Literature Review. Los Alamos National Laboratory Report, LA-13070-MS.

R. P. C. Sampaio, N. M. M. Maia, and J. M. M. Silva. 1999. Damage Detection Using the Frequency-Response-Function Curvature Method. Journal of Sound and Vibration. 226: 1029–1042.

C. R. Farrar, S. W. Doebling, and D. A. Nix. 2001. Vibration-based Structural Damage Identification. Phil. Trans. R. Soc. Lond. 359: 131–149.

M. Sahin and R. A. Shenoi. 2003. Quantification and Localisation of Damage in Beam-like Structures by Using Artificial Neural Networks With Experimental Validation. Engineering Structures. 25: 1785–1802.

T. H. Ooijevaar, R. Loendersloot, L. L. Warnet, A. d. Boer, and R. Akkerman. 2010. Vibration Based Structural Health Monitoring of a Composite T-Beam. Composite Structures. 92: 2007–2015.

K. Allbright, K. Parekh, R. Miller, and T. M. Baseheart. 1994. Modal Verification of a Destructive of a Damaged Prestressed Concrete Beam. Experimental Mechanics. 34: 389–396.

T. H. T. Chan, S. S. Law, and T. H. Yung. 2000. Moving Force Identification Using an Existing Prestressed Concrete Bridge. Engineering Structures. 22: 1261–1270.

A. Miyamoto, K. Tei, H. Nakamura, and J. W. Bull. 2000. Behavior of Prestressed Beam Strengthened with External Tendons. Journal of Structural Engineering. 126: 1033–1044.

W. X. Ren, T. Zhao, and I. E. Harik. 2004. Experimental and Analytical Modal Analysis of Steel Arch Bridge. Journal of Structural Engineering. 130: 1022–1031.

J. F. Unger, A. Teughels, and G. D. Roeck. 2006. System Identification and Damage Detection of a Prestressed Concrete Beam. Journal of Structural Engineering. 132: 1691–1698.

W. Chung and S. M. Kim. 2011. Comparison of Dynamic Properties of Spliced and Monolithic Prestressed Concrete Box Railway Girders. Engineering Structures. 33: 1773–1780.

X. H. He, X. W. Sheng, A. Scanlon, D. G. Linzell, and X. D. Yu. 2012. Skewed Concrete Box Girder Bridge Static and Dynamic Testing and Analysis. Engineering Structures. 39: 38–49.

S. Maas, A. Zürbes, D. Waldmann, M. Waltering, V. Bungard, and G. D. Roeck. 2012. Damage Assessment of Concrete Structures Through Dynamic Testing Methods. Part 1 – Laboratory Tests. Engineering Structures. 34: 351–362.

S. Alampalli. 2000. Effects of Testing, Analysis, Damage, and Environment on Modal Parameters. Mechanical Systems and Signal Processing. 14: 63–74.

C. Farrar, S. W. Doebling, P. J. Cornwell, and E. G. Straser. 1997. Variability of Modal Parameters Measured on the Alamosa Canyon Bridge. In Proceedings of XV International Modal Analysis Conference. 257–263.

Y. Q. Lin, W. X. Ren, and S. E. Fang. 2011. Structural Damage Detection Based on Stochastic Subspace Identification and Statistical Pattern Recognition: Ii. Experimental Validation Under Varying Temperature. Smart Materials And Structures. 20: 115009.

Z. R. Lu and S. S. Law. 2006. Identification of Prestress Force from Measured Structural Responses. Mechanical Systems and Signal Processing. 20: 2186–2199.

J. Zhao, J. N. Ivan, and J. T. DeWolf. 1998. Structural Damage Detection Using Artificial Neural Networks. Journal of Infrastructure Systems. 4: 93–101.

J. M. Ko, Z. G. Sun, and Y. Q. Ni. 2002. Multi-Stage Identification Scheme For Detecting Damage In Cable-Stayed Kap Shui Mun Bridge. Engineering Structures. 24: 857–868.

J. J. Lee, J. W. Lee, J. H. Yi, C. B. Yun, and H. Y. Jung. 2005. Neural Networks-Based Damage Detection For Bridges Considering Errors In Baseline Finite Element Models. Journal of Sound and Vibration. 280: 555–578.

J. Min, S. Park, C. B. Yun, C. G. Lee, and C. Lee. 2012. Impedance-based Structural Health Monitoring Incorporating Neural Network Technique For Identification of Damage Type and Severity. Engineering Structures. 39: 210–220.

British Standard. 1990. Guide to Accuracy in Building. In Problems Of Inaccuracy Or Fit Associated With Elements And Components Of Construction. BS5606.

Downloads

Published

2014-06-20

How to Cite

Factors Influencing Natural Frequencies in a Prestressed Concrete Panel for Damage Detection. (2014). Jurnal Teknologi, 69(3). https://doi.org/10.11113/jt.v69.3143