Smart Structures and Systems

  • 제14권5호
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  • Pages.917-942
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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

An integrated approach for structural health monitoring using an in-house built fiber optic system and non-parametric data analysis

  • Malekzadeh, Masoud (Department of Civil, Environmental, and Construction Engineering, University of Central Florida) ;
  • Gul, Mustafa (Department of Civil and Environmental Engineering, University of Alberta) ;
  • Kwon, Il-Bum (Center for Safety Measurements, Korea Research Institute of Standards and Science) ;
  • Catbas, Necati (Department of Civil, Environmental, and Construction Engineering, University of Central Florida)
  • 투고 : 2013.07.08
  • 심사 : 2014.02.26
  • 발행 : 2014.11.25
⟨ 이전 논문 다음 논문 ⟩

초록

Multivariate statistics based damage detection algorithms employed in conjunction with novel sensing technologies are attracting more attention for long term Structural Health Monitoring of civil infrastructure. In this study, two practical data driven methods are investigated utilizing strain data captured from a 4-span bridge model by Fiber Bragg Grating (FBG) sensors as part of a bridge health monitoring study. The most common and critical bridge damage scenarios were simulated on the representative bridge model equipped with FBG sensors. A high speed FBG interrogator system is developed by the authors to collect the strain responses under moving vehicle loads using FBG sensors. Two data driven methods, Moving Principal Component Analysis (MPCA) and Moving Cross Correlation Analysis (MCCA), are coded and implemented to handle and process the large amount of data. The efficiency of the SHM system with FBG sensors, MPCA and MCCA methods for detecting and localizing damage is explored with several experiments. Based on the findings presented in this paper, the MPCA and MCCA coupled with FBG sensors can be deemed to deliver promising results to detect both local and global damage implemented on the bridge structure.

키워드

참고문헌

  1. Aktan, A.E., Catbas, F.N., Grimmelsman, K.A. and Tsikos, C.J. (2000), "Issues in infrastructure health monitoring for management", J. Eng. Mech. - ASCE, 126(7), 711-724. https://doi.org/10.1061/(ASCE)0733-9399(2000)126:7(711)
  2. Aktan, A.E., Farhey, D.N., Brown, D.L., Dalal, V., Helmicki, A.J., Hunt, V.J. and Shelley, S.J. (1996), "Condition assessment for bridge management", J. Infrastruct. Syst., 2(3), 108-117. https://doi.org/10.1061/(ASCE)1076-0342(1996)2:3(108)
  3. Ansari, F. (2005), "Fiber optic health monitoring of civil structures using long gage and acoustic sensors", Smart Mater. Struct., 14(3), doi:10.1088/0964-1726/14/3/001.
  4. ASCE (2013), ASCE report cards for Americas infrastructure, Tech. Rep. American Society of Civil Engineers, Washington, DC, USA.
  5. Balageas, D. (2006), Introduction to structural health monitoring, 13-43, ISTE.
  6. Brownjohn, J., Tjin, S.C., Tan, G.H., Tan, B.L. and Chakraboorty, S. (2004), "A structural health monitoring paradigm for civil infrastructure", Proceedings of the 1st FIG international symposium on engineering surveys for construction works and structural engineering.
  7. Brownjohn, J.M.W., Zasso, A., Stephen, G.A. and Severn, R.T. (1995), "Analysis of experimental data from wind-induced response of a long span bridge", J. Wind Eng. Ind. Aerod., 54, 13-24.
  8. Cardini, A.J. and DeWolf, J.T. (2009), "Long-term structural health monitoring of a multi-girder steel composite bridge using strain data", Struct. Health Monit., 8(1), 47-58. https://doi.org/10.1177/1475921708094789
  9. Catbas, F.N., Gokce, H.B. and Gul, M. (2012), "Nonparametric analysis of structural health monitoring data for identification and localization of changes: Concept, lab, and real-life studies", Struct. Health Monit., 11(5), 613-626. https://doi.org/10.1177/1475921712451955
  10. Catbas, F.N., Gul, M., Zaurin, R., Gokce, H.B., Terrell, T., Dumlupinar, T. and Maier, D. (2010), Long term bridge maintenance monitoring demonstration on a movable bridge: A framework for structural health monitoring of movable bridges.
  11. Catbas, F.N., Kijewski-Correa, T. and Aktan, A.E. (2013), "Structural identification of constructed systems: Approaches, methods, and technologies for effective practice of St-Id", American Society of Civil Engineers (ASCE) Structural Engineering Institute (SEI), ISBN 978-07-8441-1971 (234 pages)
  12. Catbas, F.N. and Kijewski-Correa, T. (2013), "Structural identification of constructed systems: A collective effort toward an integrated approach that reduces barriers to adoption", J. Struct. Eng. - ASCE, 139(10), 1648-1653. https://doi.org/10.1061/(ASCE)ST.1943-541X.0000682
  13. Chang, F. K. (1999), Structural health monitoring 2000, CRC.
  14. Enright, M.P. and Frangopol, D.M. (1999), "Condition prediction of deteriorating concrete bridges using Bayesian Aupdating", J. Struct. Eng. - ASCE, 125(10), 1118-1125. https://doi.org/10.1061/(ASCE)0733-9445(1999)125:10(1118)
  15. 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
  16. Ferdinand, P., Magne, S., Dewynter-Marty, V., Martinez, C., Rougeault, S. and Bugaud, M. (1997), Applications of Bragg grating sensors in Europe, In Optical Fiber Sensors, Optical Society of America.
  17. Glisic, B. and Inaudi, D. (2008), Fibre optic methods for structural health monitoring,Wiley-Interscience.
  18. Hill, K.O., Fujii, Y., Johnson, D.C. and Kawasaki, B.S. (1978), "Photosensitivity in optical fiber waveguides: Application to reflection filter fabrication", Appl. Phys. Lett., 32, 647. https://doi.org/10.1063/1.89881
  19. Inaudi, D. (2002), Photonic sensing technology in civil engineering applications, Handbook of Optical Fibre Sensing Technology, Chichester.
  20. Kwon, I.B., Baik, S.J., Im, K. and Yu, J.W. (2002), "Development of fiber optic BOTDA sensor for intrusion detection", Sensors Actuat. A - Phys., 101(1), 77-84. https://doi.org/10.1016/S0924-4247(02)00184-X
  21. Kwon, I.B., Malekzadeh, M., Ma, Q., Gokce, H., Terrell, T.K., Fedotov, A. and Catbas, F.N. (2011), Fiber optic sensor installation for monitoring of 4 span model bridge in UCF, Rotating Machinery, Structural Health Monitoring, Shock and Vibration, Springer New York.
  22. Johnson, G.A., Pran, K., Wang, G., Havsgard, G.B. and Vohra, S. (1999), "Structural monitoring of a composite hull air cushion catamaran with a multi-channel fiber Bragg grating sensor system", Proceedings of the 2nd Int. Workshop on Structural Health Monitoring, Stanford, CA .
  23. Lanata, F. (2005), Damage detection algorithms for continuous static monitoring of structures (Doctoral dissertation, PhD Thesis Italy University of Genoa DISEG).
  24. Laory, I., Trinh, T.N. and Smith, I.F. (2011), "Evaluating two model-free data interpretation methods for measurements that are influenced by temperature", Adv. Eng. Inform., 25(3), 495-506. https://doi.org/10.1016/j.aei.2011.01.001
  25. Malekzadeh, M. and Catbas, F.N. (2012), "Monitoring of bridge performance by Brillouin Optical Time Domain Analysis ( BOTDA) Sensig", Proceedings of the Joint Conference of the Engineering Mechanics Institute and 11th ASCE Joint Specialty Conference on Probabilistic Mechanics and Structural Reliability (EMI/PMC 2012), Notre Dame, Indiana, US.
  26. Malekzadeh, M., Gul, M. and Catbas, F.N. (2012), Use of FBG sensors to detect damage from large amount of dynamic measurements, In Topics on the Dynamics of Civil Structures, Springer, US.
  27. Malekzadeh, M., Gul, M. and Catbas, F.N. (2013), "Application of multivariate statistically based algorithms for civil structures anomaly detection", Proceedings of the 31th International Modal Analysis Conference, Feb 11-14, Garden Grove, California, USA.
  28. Meltz, G., Morey, W. and Glenn, W. H. (1989), "Formation of Bragg gratings in optical fibers by a transverse holographic method", Opt. Lett., 14(15), 823-825. https://doi.org/10.1364/OL.14.000823
  29. Morey, W.W., Meltz, G. and Glenn, W.H. (1990), "Fiber optic Bragg grating sensors", Proceedings of the OE/FIBERS'89 , International Society for Optics and Photonics.
  30. Mujica, L.E., Rodellar, J., Fernandez, A. and Guemes, A. (2011), "Q-statistic and T2-statistic PCA-based measures for damage assessment in structures", Struct. Health Monit., 10(5), 539-553. https://doi.org/10.1177/1475921710388972
  31. Robert-Nicoud, Y., Raphael, B. and Smith, I.F. (2005), "System identification through model composition and stochastic search", J. Comput. Civil. Eng., 19(3), 239-247. https://doi.org/10.1061/(ASCE)0887-3801(2005)19:3(239)
  32. Omenzetter, P., Brownjohn, J.M.W. and Moyo, P. (2004), "Identification of unusual events in multi-channel bridge monitoring data", Mech. Syst. Signal Pr., 18(2), 409-430. https://doi.org/10.1016/S0888-3270(03)00040-2
  33. Posenato, D., Lanata, F., Inaudi, D. and Smith, I.F. (2008), "Model-free data interpretation for continuous monitoring of complex structures", Adv. Eng. Inform., 22(1), 135-144. https://doi.org/10.1016/j.aei.2007.02.002
  34. Puentes, R. (2008), A bridge to somewhere: Rethinking American transportation for the 21st century.
  35. Richards, W.L., Parker, J., Ko, W.L., Piazza, A. and Chan, P. (2012), Application of fiber optic instrumentation (Validation des systemesd'instrumentation a fibresoptiques).
  36. Sohn, H., Farrar, C.R., Hemez, F.M., Shunk, D.D., Stinemates, D.W., Nadler, B.R. and Czarnecki, J.J. (2004), A review of structural health monitoring literature: 1996-2001 (p. 303). Los Alamos,, New Mexico: Los Alamos National Laboratory.
  37. Todd, M.D., Johnson, G.A., Chang, C.C. and Malsawma, L. (2000), "Real-time girder deflection reconstruction using a fiber Bragg grating system", Proceedings of the Int. Modal. Analysis, Conf. XVIII.
  38. Todd, M., Johnson, G., Vohra, S., Chen-Chang, C., Danver, B. and Malsawma, L. (1999), "Civil infrastructure monitoring with fiber Bragg grating sensor arrays", Proceedings of the 2nd Int. Workshop on Structural Health Monitoring , Stanford University, Stanford, CA, September 8-10.
  39. Vohra, S.T., Todd, M.D., Johnson, G.A., Chang, C.C. and Danver, B.A. (1999), "Fiber Bragg grating sensor system for civil structure monitoring: applications and field tests", Proceedings of the Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series.
  40. Worden, K. (1997), "Structural fault detection using a novelty measure", J. Sound Vib., 201(1), 85-101. https://doi.org/10.1006/jsvi.1996.0747
  41. Worden, K. and Dulieu-Barton, J.M. (2004), "An overview of intelligent fault detection in systems and structures", Struct. Health Monit., 3(1), 85-98. https://doi.org/10.1177/1475921704041866
  42. Yin, S., Ruffin, P.B. and Francis, T.S. (2008), Fiber optic sensors , CRC Press ILlc.
  43. Zaurin, R. and Catbas, F.N. (2011), "Structural health monitoring using video stream, influence lines, and statistical analysis", Struct. Health Monit., 10(3), 309-332. https://doi.org/10.1177/1475921710373290

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