Modeling of environmental influence in structural health assessment for reinforced concrete buildings

Technical Papers

Abstract

One branch of structural health monitoring (SHM) utilizes dynamic response measurements to assess the structural integrity of civil infrastructures. In particular, modal frequency is a widely adopted indicator for structural damage since its square is proportional to structural stiffness. However, it has been demonstrated in various SHM projects that this indicator is substantially affected by fluctuating environmental conditions. In order to provide reliable and consistent information on the health status of the monitored structures, it is necessary to develop a method to filter this interference. This study attempts to model and quantify the environmental infl uence on the modal frequencies of reinforced concrete buildings. Daily structural response measurements of a twenty-two story reinforced concrete building were collected and analyzed over a one-year period. The Bayesian spectral density approach was utilized to identify the modal frequencies of this building and it was clearly seen that the temperature and humidity fluctuation induced notable variations. A mathematical model was developed to quantify the environmental effects and model complexity was taken into consideration. Based on a Timoshenko beam model, the full model class was constructed and other reduced-order model class candidates were obtained. Then, the Bayesian modal class selection approach was employed to select the one with the most suitable complexity. The proposed model successfully characterizes the environmental influence on the modal frequencies. Furthermore, the estimated uncertainty of the model parameters allows for assessment of the reliability of the prediction. This study not only improves the understanding about the monitored structure, but also establishes a systematic approach for reliable health assessment of reinforced concrete buildings.

Keywords

Bayesian inference model selection reinforced concrete building structural health monitoring temperature and humidity effects Timoshenko beam 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Alampalli S (1998), “Influence of In-service Environment on Modal Parameters,” Proceedings of the 16th International Modal Analysis Conference, pp. 111–116.Google Scholar
  2. Andrade C, Sarria J and Alonso C (1999), “Relative Humidity in the Interior of Concrete Exposed to Natural and Artificial Weathering,” Cement and Concrete Research, 29(8): 1249–1259.CrossRefGoogle Scholar
  3. Askegaard V and Mossing P (1988), “Long Term Observation of RC-Bridge Using Changes in Natural Frequencies,” Nordic Concrete Research, 7: 20–27.Google Scholar
  4. Beck JL and Katafygiotis LS (1998), “Updating Models and Their Uncertainties. I: Bayesian Statistical Framework,” Journal of Engineering Mechanics, 124(4): 455–461.CrossRefGoogle Scholar
  5. Beck JL and Yuen KV (2004), “Model Selection Using Response Measurements: Bayesian Probabilistic Approach,” Journal of Engineering Mechanics, 130(2): 192–203.CrossRefGoogle Scholar
  6. Box GEP and Tiao GC (1992), Bayesian Inference in Statistical Analysis, New York: Wiley.Google Scholar
  7. Catbas FN, Susoy M and Frangopol DM (2008), “Structural Health Monitoring and Reliability Estimation: Long Span Truss Bridge Application with Environmental Monitoring Data,” Engineering Structures, 30(9): 2347–2359.CrossRefGoogle Scholar
  8. Clark SK (1972), Dynamic of Continuous Elements, Englewood Cliffs: Prentice-Hall.Google Scholar
  9. Clinton JF, Bradford SC, Heaton TH and Favela J (2006), “The Observed Wander of the Natural Frequencies in a Structure,” Bulletin of the Seismological Society of America, 96(1): 237–257.CrossRefGoogle Scholar
  10. Deng Y, Ding Y and Li A (2010), “Structural Condition Assessment of Long-span Suspension Bridges Using Long-term Monitoring Data,” Earthquake Engineering and Engineering Vibration, 9(1): 123–131.CrossRefGoogle Scholar
  11. Gull SF (1988), “Bayesian Inductive Inference and Maximum Entropy,” Maximum Entropy and Bayesian Methods in Science and Engineering, pp. 53–74.Google Scholar
  12. Kapur KK (1966), “Vibrations of a Timoshenko Beam Using Finite-element Approach,” Journal of the Acoustical Society of America, 40(5): 1058–1063.CrossRefGoogle Scholar
  13. Katafygiotis LS and Yuen KV (2001), “Bayesian Spectral Density Approach for Modal Updating Using Ambient Data,” Earthquake Engineering and Structural Dynamics, 30(8): 1103–1123.CrossRefGoogle Scholar
  14. Kim JT, Yun CB and Yi JH (2003), “Temperature Effects on Frequency-based Damage Detection in Plate-Girder Bridges,” KSCE Journal of Civil Engineering, 7(6): 725–733.CrossRefGoogle Scholar
  15. Mangat PS and Limbachiya MK (1995), “Repair Material Properties Which Influence Long-term Performance of Concrete Structures,” Construction and Building Materials, 9(2): 81–90.CrossRefGoogle Scholar
  16. Naus DJ (2006), “The Effect of Elevated Temperature on Concrete Materials and Structures — A Literature Review,” Report of Oak Ridge National Laboratory, U.S. Nuclear Regulatory Commissions Office of Nuclear Regulatory Research Washington, DC 20555-0001.Google Scholar
  17. Papadimitriou C, Beck JL and Katafygiotis LS (1997), “Asymptotic Expansions for Reliability and Moments of Uncertain Systems,” Journal of Engineering Mechanics, 123(12): 1219–1229.CrossRefGoogle Scholar
  18. Parrott LJ (1988), “Moisture Profiles in Drying Concrete,” Advance in Cement Research, 1(3): 164–170.Google Scholar
  19. Peeters B and De Roeck G (2001), “One-year Monitoring of the Z24-Bridge: Environmental Effects Versus Damage Events,” Earthquake Engineering and Structural Dynamics, 30(2): 149–171.CrossRefGoogle Scholar
  20. Rincón OT, Hernández-López Y, Valle-Moreno A, Torres-Acosta AA, Barrios F, Montero P, Oidor-Salinas P and Montero JR (2008), “Environmental Influence on Point Anodes Performance in Reinforced Concrete,” Construction and Building Materials, 22(4): 494–503.CrossRefGoogle Scholar
  21. Salawu OS (1997), “Detection of Structural Damage through Changes in Frequency: A Review,” Engineering Structures, 19(9): 718–723.CrossRefGoogle Scholar
  22. Shibata R (1986), “Consistency of Model Selection and Parameter Estimation,” Journal of Applied Probability, 23(A): 127–141.CrossRefGoogle Scholar
  23. Smith BS and Coull A (1991), Tall Building Structures: Analysis and Design, New York: Wiley.Google Scholar
  24. Sohn H, Farrar CR, Hemez FM, Shunk DD, Stinemates DW and Nadler BR (2003), “A Review of Structural Health Monitoring Literature: 1996–2001,” Los Alamos National Laboratory Report LA-13976-MS.Google Scholar
  25. Tazawa EI and Miyazawa S (1995), “Experimental Study on Mechanism of Autogenous Shrinkage of Concrete,” Cement and Concrete Research, 25(8): 1633–1638.CrossRefGoogle Scholar
  26. Timoshenko SP (1922), “On the Transverse Vibrations of Bars of Uniform Cross-section,” Philosophical Magazine, 43: 125–131.Google Scholar
  27. Weaver WJR, Timoshenko SP and Young DH (1990), Vibration Problems in Engineering, 5th ed., New York: Wiley.Google Scholar
  28. Wittrick WH and Williams FW (1971), “A General Algorithm for Computing Natural Frequencies of Elastic Structures,” Quarterly Journal of Mechanics and Applied Mathematics, 24(3): 263–284.CrossRefGoogle Scholar
  29. Xia Y, Hao H, Zanardo G and Deeks A (2006), “Long Term Vibration Monitoring of an RC Slab: Temperature and Humidity Effect,” Engineering Structures, 28(3): 441–452.CrossRefGoogle Scholar
  30. Yuen KV (2010), Bayesian Methods for Structural Dynamics and Civil Engineering, John Wiley and Sons.Google Scholar
  31. Yuen KV and Beck JL (2003), “Updating Properties of Nonlinear Dynamical Systems with Uncertain Input,” Journal of Engineering Mechanics, 129(1): 9–20.CrossRefGoogle Scholar
  32. Yuen KV, Katafygiotis LS and Beck JL (2002) “Spectral Density Estimation of Stochastic Vector Processes,” Probabilistic Engineering Mechanics, 17(3): 265–272.CrossRefGoogle Scholar
  33. Zellner A, Keuzenkamp HA and McAleer M (2001), Simplicity, Inference and Modeling: Keeping it Sophisticatedly Simple, Cambridge: Cambridge University Press.Google Scholar

Copyright information

© Institute of Engineering Mechanics, China Earthquake Administration and Springer Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Department of Civil and Environmental EngineeringUniveristy of MacauMacauChina
  2. 2.Faculty of Science and TechnologyUniversity of MacauMacaoChina

Personalised recommendations