Smart Structures and Systems

  • 제6권1호
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  • Pages.17-27
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  • 2010
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
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Statistical approach to a SHM benchmark problem

  • 투고 : 2007.04.15
  • 심사 : 2009.05.14
  • 발행 : 2010.01.25
⟨ 이전 논문 다음 논문 ⟩

초록

The approach to damage detection and localization adopted in this paper is based on a statistical comparison of models built from the response time histories collected at different stages during the structure lifetime. Some of these time histories are known to have been recorded when the structural system was undamaged. The consistency of the models associated to two different stages, both undamaged, is first recognized. By contrast, the method detects the discrepancies between the models from measurements collected for a damaged situation and for the undamaged reference situation. The damage detection and localization is pursued by a comparison of the SSE (sum of the squared errors) histograms. The validity of the proposed approach is tested by applying it to the analytical benchmark problem developed by the ASCE Task Group on Structural Health Monitoring (SHM). In the paper, the results of the benchmark studies are presented and the performance of the method is discussed.

키워드

과제정보

연구 과제 주관 기관 : Athenaeum Research Fund of the University of Catania

참고문헌

  1. Breitung, K. and Faravelli, L. (1996), Response surface methods and asymptotic approximations, Chapter 6 in F. Casciati and J.B. Roberts (eds.), Mathematical Models for Structural Reliability Analysis, CRC Press, Boca Raton, USA, 227-285.
  2. Casciati, F. and Casciati, S. (2006), "Structural health monitoring by lyapunov exponents of non-linear time series", Struct. Control Health Monit., 13(1), 132-146. https://doi.org/10.1002/stc.141
  3. Casciati, S. (2004), "Statistical models comparison for damage detection using the ASCE benchmark", Proc. EWSHM, DEStech Publications, Lancaster, Pennsylvania, USA, 695-702.
  4. Casciati, S. (2005), Data Detection and Localization in the Space of the Observed Data, PhD Thesis, Graduate School of Civil Engineering, University of Pavia, Italy.
  5. Casciati, S. (2008), "Stiffness identification and damage localization via differential evolution algorithms", Struct. Control Health Monit., 15(3), 436-449. https://doi.org/10.1002/stc.236
  6. Casciati, S. (2009), "Using response surface models to detect and localize distributed cracks in a complex continuum", submitted for publication.
  7. Draper, N. and Smith, H. (1981), Applied Regression Analysis, Wiley, New York.
  8. Johnson, E.A., Lam, H.F., Katafygiotis, L.S. and Beck, J.L. (2000), "A benchmark problem for structural health monitoring and damage detection", 14th Engineering Mechanics Conf., Austin, Texas, May 2000, ASCE.
  9. Johnson, E.A., Lam, H.F., Katafygiotis, L.S. and Beck, J.L. (2004), "Phase I IASC-ASCE structural health monitoring benchmark using simulated data", J. Eng. Mech. ASCE, 130(1), 3-15. https://doi.org/10.1061/(ASCE)0733-9399(2004)130:1(3)
  10. Masri, S.F., Smyth, A.W., Chassiakos, A.G., Caughey, T.K. and Hunter, N.F. (2000), "Application of neural networks for detection of changing in nonlinear systems", J. Eng. Mech. ASCE, 126(1), 666-683. https://doi.org/10.1061/(ASCE)0733-9399(2000)126:7(666)

피인용 문헌

  1. Nonparametric statistical formulations for structural health monitoring vol.148, 2015, https://doi.org/10.1016/j.compstruc.2014.10.007
  2. Damage detection in beam and truss structures by the inverse analysis of the static response due to moving loads vol.24, pp.10, 2017, https://doi.org/10.1002/stc.1972
  3. Study on Damage Detection Method using Meta Model vol.29, pp.5, 2015, https://doi.org/10.5574/KSOE.2015.29.5.351
  4. Photogrammetry in Documentation and Ambient Vibration Test of Historical Masonry Minarets vol.40, pp.6, 2016, https://doi.org/10.1007/s40799-016-0137-2
  5. Output-error state-space identification of vibrating structures using evolution strategies: a benchmark study vol.14, pp.1, 2014, https://doi.org/10.12989/sss.2014.14.1.017
  6. An enhanced energy vibration-based approach for damage detection and localization vol.25, pp.1, 2018, https://doi.org/10.1002/stc.2047
  7. A degree of dispersion-based damage localization method vol.23, pp.1, 2016, https://doi.org/10.1002/stc.1760
  8. Regression models for structural health monitoring of welded bridge joints based on temperature, traffic and strain measurements vol.14, pp.6, 2015, https://doi.org/10.1177/1475921715609801
  9. Damage detection on a three-storey steel frame using artificial neural networks and genetic algorithms vol.50, pp.3, 2015, https://doi.org/10.1007/s11012-014-0085-9
  10. A multi-reference-based mode selection approach for the implementation of NExT-ERA in modal-based damage detection vol.21, pp.8, 2014, https://doi.org/10.1002/stc.1638
  11. A Multi-Step Approach for Identification of Structural Modifications Based on Operational Modal Analysis vol.14, pp.05, 2014, https://doi.org/10.1142/S0219455414400045
  12. Optimal sensor placement methodology for uncertainty reduction in the assessment of structural condition vol.24, pp.6, 2017, https://doi.org/10.1002/stc.1927
  13. Crack Identification of Cantilever Plates Based on a Kriging Surrogate Model vol.135, pp.5, 2013, https://doi.org/10.1115/1.4023813
  14. Energy harvesting and power management of wireless sensors for structural control applications in civil engineering vol.10, pp.3, 2010, https://doi.org/10.12989/sss.2012.10.3.299
  15. Crack identification based on Kriging surrogate model vol.41, pp.1, 2010, https://doi.org/10.12989/sem.2012.41.1.025