Smart Structures and Systems

  • 제13권5호
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  • Pages.849-868
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  • 2014
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Indirect structural health monitoring of a simplified laboratory-scale bridge model

  • Cerda, Fernando (Universidad de Concepcion) ;
  • Chen, Siheng (Department of Electrical and Computer Engineering, Carnegie Mellon University) ;
  • Bielak, Jacobo (Department of Civil and Environmental Engineering, Carnegie Mellon University) ;
  • Garrett, James H. (Department of Civil and Environmental Engineering, Carnegie Mellon University) ;
  • Rizzo, Piervincenzo (Department of Civil and Environmental Engineering, University of Pittsburgh) ;
  • Kovacevic, Jelena (Department of Electrical and Computer Engineering, Carnegie Mellon University)
  • 투고 : 2012.05.18
  • 심사 : 2013.08.22
  • 발행 : 2014.05.25
⟨ 이전 논문 다음 논문 ⟩

초록

An indirect approach is explored for structural health bridge monitoring allowing for wide, yet cost-effective, bridge stock coverage. The detection capability of the approach is tested in a laboratory setting for three different reversible proxy types of damage scenarios: changes in the support conditions (rotational restraint), additional damping, and an added mass at the midspan. A set of frequency features is used in conjunction with a support vector machine classifier on data measured from a passing vehicle at the wheel and suspension levels, and directly from the bridge structure for comparison. For each type of damage, four levels of severity were explored. The results show that for each damage type, the classification accuracy based on data measured from the passing vehicle is, on average, as good as or better than the classification accuracy based on data measured from the bridge. Classification accuracy showed a steady trend for low (1-1.75 m/s) and high vehicle speeds (2-2.75 m/s), with a decrease of about 7% for the latter. These results show promise towards a highly mobile structural health bridge monitoring system for wide and cost-effective bridge stock coverage.

키워드

과제정보

연구 과제 주관 기관 : Carnegie Mellon University

참고문헌

  1. Bu, J.Q., Law, S.S. and Zhu, X.Q. (2006), "Innovative bridge condition assessment from dynamic response of a passing vehicle", J. Eng. Mech - ASCE, 132(12), 1372-1379. https://doi.org/10.1061/(ASCE)0733-9399(2006)132:12(1372)
  2. Carden, E.P. and Fanning, P. (2004), "Vibration based condition monitoring: a review", Struct. Health Monit., 3(4), 355-377. https://doi.org/10.1177/1475921704047500
  3. Casciati, F. and Giordano, M. (2010), "Structural Health Monitoring", Proceedings of the 5th European Workshop on Structural Health Monitoring Held at Sorrento, Naples, Itlay, June 28-july 4, DEStech Publications, Inc.
  4. Chang, F.K. (2011), "Structural Health Monitoring", Proceedings of the Condition-Based Maintenance and Intelligent Structures, Destech Publications.
  5. Chajes, J., Mertz, D. and Commander, B. (1997), "Experimental load rating of a posted bridge", J. Bridge Eng., 2(1), 1-10. https://doi.org/10.1061/(ASCE)1084-0702(1997)2:1(1)
  6. Doebling, S.W., Farrar, Ch.R. and Prime, M.B. (1998), "A summary review of Vibration-based Damage Identification Methods", Shock Vib.Digest , 30, 91-105. https://doi.org/10.1177/058310249803000201
  7. Duda, R.O., Hart, P.E. and Stork, D.G. (2000), Pattern classification (2nd Ed.), Wiley-Interscience.
  8. Farhey, D.N. (2005), "Bridge instrumentation and monitoring for structural diagnostics", Struct Health Monit., 4(4), 301-318. https://doi.org/10.1177/1475921705057966
  9. Farhey, D.N (2007), "Quantitative assessment and forecast for structurally deficient bridge diagnostics", Struct Health Monit., 6(1), 39-48. https://doi.org/10.1177/1475921707072061
  10. Farrar, C.R. and Worden, K. (2007), "An introduction to structural health monitoring", Philos. T. R. Soc. A, 365(1851), 303-315. https://doi.org/10.1098/rsta.2006.1928
  11. FHWA (2011), Deficient bridges by state and highway system", http://www.fhwa.dot.gov/bridge/deficient.cfm.
  12. Frangopol, D.M., Strauss, A. and Kim, S. (2008), "Bridge reliability assessment based on monitoring", J. Bridge Eng., 13(3), 258-270. https://doi.org/10.1061/(ASCE)1084-0702(2008)13:3(258)
  13. Frangopol, D.M., Sause, R. and Kusko, Ch. (Eds.) (2010), "Bridge Maintenance, Safety and Management - IABMAS'10", Proceedings of the 5th International IABMAS Conference, Philadelphia, USA, 11-15 July, 1st ed. CRC Press.
  14. Gonzalez, A., OBrien, E.J. and McGetrick, P.J. (2012), "Identification of damping in a bridge using a moving instrumented vehicle", J. Sound Vib., 331(18), 4115-4131. https://doi.org/10.1016/j.jsv.2012.04.019
  15. Isemoto, R., Kim, C.W. and Sugiura, K. (2010), "Abnormal diagnosis of bridges using traffic-induced vibration measurements", Proceedings of the 23th KKCNN Symposium on Civil Engineering, Taipei, Taiwan.
  16. Keeler, R.J. and Passarelli, R.E. (1989), Signal processing for atmospheric radars, NCAR/TN-331+STR NCAR TECHNICAL NOTE.
  17. Kim, C.W. and Kawatani, M. (2008), "Pseudo-static approach for damage identification of bridges based on coupling vibration with a moving vehicle", Struct. Infrastruct. E., 4(5), 371-379. https://doi.org/10.1080/15732470701270082
  18. Kim, C.W., Kawatani, M. and Fujimoto, T. (2010), "Identifying bending stiffness change of a beam under a moving vehicle", In Bridge Maintenance, Safety and Management - IABMAS'10, 180-180, Bridge Maintenance, Safety and Management. CRC Press.
  19. Kim, Y.J., Tanovic, R. and Wight, R.G. (2009), "Recent advances in performance evaluation and flexural response of existing bridges", J. Perform. Constr. Fac., 23(3), 190-200. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000007
  20. Kullback, S, and Leibler, R.A. (1951), "On Information and Sufficiency", Ann. Math. Statist , 22, 79-86. https://doi.org/10.1214/aoms/1177729694
  21. Lin, C.W. and Yang, Y.B. (2005), "Use of a passing vehicle to scan the fundamental bridge frequencies: an experimental verification", Eng. Struct., 27(13), 1865-1878. https://doi.org/10.1016/j.engstruct.2005.06.016
  22. McGetrick, P.J., Gonzalez, A. and O'Brien, E.J. (2009), "Theoretical investigation of the use of a moving vehicle to identify bridge dynamic parameters", Insight: Non-Destructive Testing and Condition Monitoring , 51(8), 433-438. https://doi.org/10.1784/insi.2009.51.8.433
  23. McGetrick, P.J., Kim, C.W. and Obrien, E.J. (2010), "Experimental investigation of the detection of bridge dynamic parameters using a moving vehicle", Proceedings of the 23th KKCNN Symposium on Civil Engineering, Taipei, Taiwan.
  24. Miyamoto, A., and Yabe, A. (2011). "Bridge condition assessment based on vibration responses of passenger vehicle", J. Phys. Conf. Ser., 305(1), doi:10.1088/1742-6596/305/1/012103.
  25. Rytter, A. (1993), Vibration based inspection of civil engineering structures, Ph.D. Dissertation, Department of Building Technology and Structural Engineering, Aalborg University, Denmark.
  26. Siringoringo, D.M. and Fujino, Y. (2012), "Estimating bridge fundamental frequency from vibration response of instrumented passing vehicle: analytical and experimental study", Adv. Struct. Eng., 15(3), 417-433. https://doi.org/10.1260/1369-4332.15.3.417
  27. Yang, Y.B. and Chang, K.C. (2009), "Extraction of bridge frequencies from the dynamic response of a passing vehicle enhanced by the EMD technique", J. Sound Vib., 322(4-5), 718-739. https://doi.org/10.1016/j.jsv.2008.11.028
  28. Yang, Y.B., Lin, C.W. and Yau, J.D. (2004), "Extracting bridge frequencies from the dynamic response of a passing vehicle", J. Sound Vib., 272(3-5), 471-493. https://doi.org/10.1016/S0022-460X(03)00378-X
  29. Yang, Y.B. and Lin, C.W. (2005), "Vehicle-bridge interaction dynamics and potential applications", J. Sound Vib., 284(1-2), 205-226. https://doi.org/10.1016/j.jsv.2004.06.032
  30. Yin, S.H, and Tang, C.Y. (2011), "Identifying cable tension loss and deck damage in a cable-stayed bridge using a moving vehicle", J. Vib. Acoust., 133(2), 021007. https://doi.org/10.1115/1.4002128

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  5. A Review of Indirect Bridge Monitoring Using Passing Vehicles vol.2015, 2015, https://doi.org/10.1155/2015/286139
  6. Stochastic characteristics of estimated frequencies in bridge–vehicle interactions vol.5, pp.3, 2015, https://doi.org/10.1007/s13349-015-0101-3
  7. Extraction of Bridge Frequencies from a Moving Test Vehicle by Stochastic Subspace Identification vol.21, pp.3, 2016, https://doi.org/10.1061/(ASCE)BE.1943-5592.0000792
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  9. Semi-Supervised Multiresolution Classification Using Adaptive Graph Filtering With Application to Indirect Bridge Structural Health Monitoring vol.62, pp.11, 2014, https://doi.org/10.1109/TSP.2014.2313528
  10. State-of-the-Art Review on Modal Identification and Damage Detection of Bridges by Moving Test Vehicles vol.18, pp.02, 2018, https://doi.org/10.1142/S0219455418500256
  11. NEOFog : Nonvolatility-Exploiting Optimizations for Fog Computing vol.53, pp.2, 2018, https://doi.org/10.1145/3296957.3177154
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  14. Frequency Estimation on Two-Span Continuous Bridges Using Dynamic Responses of Passing Vehicles vol.146, pp.1, 2020, https://doi.org/10.1061/(asce)em.1943-7889.0001698
  15. Inverse Filtering for Frequency Identification of Bridges Using Smartphones in Passing Vehicles: Fundamental Developments and Laboratory Verifications vol.20, pp.4, 2014, https://doi.org/10.3390/s20041190
  16. Vehicle-Assisted Techniques for Health Monitoring of Bridges vol.20, pp.12, 2020, https://doi.org/10.3390/s20123460
  17. Frequency Identification of Bridges Using Smartphones on Vehicles with Variable Features vol.25, pp.7, 2020, https://doi.org/10.1061/(asce)be.1943-5592.0001565
  18. The Way Forward for Indirect Structural Health Monitoring (iSHM) Using Connected and Automated Vehicles in Europe vol.6, pp.3, 2021, https://doi.org/10.3390/infrastructures6030043
  19. Examining changes in bridge frequency due to damage using the contact-point response of a passing vehicle vol.6, pp.3, 2021, https://doi.org/10.1080/24705314.2021.1906088
  20. Drive-by methodology to identify resonant bridges using track irregularity measured by high-speed trains vol.158, pp.None, 2014, https://doi.org/10.1016/j.ymssp.2021.107667