Smart Structures and Systems

  • 제30권4호
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  • Pages.339-351
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  • 2022
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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

Structural system identification by measurement error-minimization observability method using multiple static loading cases

  • Lei, Jun (Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration) ;
  • Lozano-Galant, Jose Antonio (Department of Civil Engineering, University of Castilla-La Mancha) ;
  • Xu, Dong (Department of Bridge Engineering, Tongji University) ;
  • Zhang, Feng-Liang (Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration) ;
  • Turmo, Jose (Department of Civil and Environmental Engineering, Universitat Politecnica de Catalunya)
  • 투고 : 2021.06.20
  • 심사 : 2022.05.26
  • 발행 : 2022.10.25
⟨ 이전 논문 다음 논문 ⟩

초록

Evaluating the current condition of existing structures is of primary importance for economic and safety reasons. This can be addressed by Structural System Identification (SSI). A reliable static SSI depends on well-designed sensor configuration and loading cases, as well as efficient parameter estimation algorithms. Static SSI by the Measurement Error-Minimizing Observability Method (MEMOM) is a model-based deterministic static SSI method that could estimate structural parameters from static responses. In the current state of the art, this method is only applicable when structures are subjected to one loading case. This might lead to lack of information in some local regions of the structure (such as the null curvatures zones). To address this issue, the SSI by MEMOM using multiple loading cases is proposed in this work. Observability equations obtained from different loading cases are concatenated simultaneously and an optimization procedure is introduced to obtain the estimations by minimizing the discrepancy between the predicted response and the measured one. In addition, a Genetic-Algorithm (GA)-based Optimal Sensor Placement (OSP) method is proposed to tackle the OSP problem under multiple static loading cases for the very first time. In this approach, the Fisher Information Matrix (FIM)'s determinant is used as the metric of the goodness of sensor configurations. The numerical examples of a 3-span continuous bridge and a 13-story frame, are analyzed to validate the applicability of the extended SSI by MEMOM and the GA-based OSP method.

키워드

과제정보

This work was partially funded by the Scientific Research Fund of Institute of Engineering Mechanics, China Earthquake Administration (Grant No. 2019 EEEVL0401); Natural Science Foundation of Shenzhen (Grant No: JCYJ20190806143618723); Spanish Ministry of Economy and Competitiveness and FEDER fund (Grant No: BIA2017-86811-C2-1-R, directed by Jose Turmo, and Grant No: BIA2017-86811-C2-2-R). Authors are also indebted to the Secretaria d' Universitats i Recerca de la Generalitat de Catalunya for the funding provided through Agaur (Grant No: 2017 SGR 1481). Part of this work was done through a collaborative agreement between Tongji University (China) and Technical University of Catalonia, UPC. The financial support from the Chinese High-End Foreign Experts program (GDW20143100115) is greatly appreciated. Funding for this research has been provided to Jun Lei by the Chinese Scholarship Council through its program No.201506260116 and by the Spanish Ministry of Economy and Competitiveness through its program BES-2014-07022 for his PhD stays.

참고문헌

  1. Abdo, M.A.-B. (2012), "Parametric study of using only static response in structural damage detection", Eng. Struct., 34, 124-131. https://doi.org/10.1016/j.engstruct.2011.09.027
  2. Bakhtiari-Nejad, F., Rahai, A. and Esfandiari, A. (2005), "A structural damage detection method using static noisy data", Eng. Struct., 27(12), 1784-1793. https://doi.org/10.1016/j.engstruct.2005.04.019
  3. Banan, M.R., Banan, M.R. and Hjelmstad, K.D. (1994a), "Parameter estimation of structures from static response. II: Numerical simulation studies", J. Struct. Eng., 120(11), 3259-3283. https://doi.org/10.1061/(ASCE)0733-9445(1994)120:11(3259)
  4. Banan, M.R., Banan, M.R. and Hjelmstad, K.D. (1994b), "Parameter estimation of structures from static response. I. Computational aspects", J. Struct. Eng., 120(11), 3243-3258. https://doi.org/10.1061/(ASCE)0733-9445(1994)120:11(3243)
  5. Bertola, N.J., Papadopoulou, M., Vernay, D. and Smith, I.F.C. (2017), "Optimal multi-type sensor placement for structural identification by static-load testing", Sensors, 17(12). https://doi.org/10.3390/s17122904
  6. Brownjohn, J., Fujino, Y., Inaudi, D. and Wu, Z. (2008), "Structural identification of constructed systems: experimental considerations", Proceedings of ASCE Structures Congress 2008, Vancouver, Canada, April, pp. 1-13. https://doi.org/10.1061/41016(314)139
  7. Chen, X.Z., Zhu, H.P. and Chen, C.Y. (2005), "Structural damage identification using test static data based on grey system theory", J. Zhejiang Univ. Sci., 6A(8), 790-796. https://doi.org/10.1631/jzus.2005.A0790
  8. Deep, K., Singh, K.P., Kansal, M.L. and Mohan, C. (2009), "A real coded genetic algorithm for solving integer and mixed integer optimization problems", Appl. Math. Comput., 212(2), 505-518. https://doi.org/10.1016/j.amc.2009.02.044
  9. Emadi, S., Lozano-Galant, J.A., Xia, Y., Ramos, G. and Turmo, J. (2019), "Structural system identification including shear deformation of composite bridges from vertical deflections", Steel Compos. Struct., Int. J., 32(6), 731-741. https://doi.org/10.12989/scs.2019.32.6.731
  10. Farrar, C.R., Baker, W.E., Bell, T.M., Cone, K.M., Darling, T.W., Duffey, T.A., Eklund, A. and Migliori, A. (1994), "Dynamic characterization and damage detection in the I-40 bridge over the Rio Grande", Research Report No. LA-12767-MS; Los Alamos National Laboratory, USA. https://doi.org/10.2172/10158042
  11. Hajela, P. and Soeiro, F.J. (1990), "Recent developments in damage detection based on system identification methods", Struct. Optimiz., 2(1), 1-10. https://doi.org/10.1007/BF01743515
  12. Hjelmstad, K.D. and Shin, S. (1997), "Damage detection and assessment of structures from static response", J. Eng. Mech., 123(6), 568-576. https://doi.org/10.1061/(ASCE)0733-9399(1997)123:6(568)
  13. Kim, J.-T., Ryu, Y.-S., Cho, H.-M. and Stubbs, N. (2003), "Damage identification in beam-type structures: frequencybased method vs mode-shape-based method", Eng. Struct., 25(1), 57-67. https://doi.org/10.1016/S0141-0296(02)00118-9
  14. Kourehli, S.S. (2017), "Plate-like structures damage detection based on static response and static strain energy using gaussian process regression (GPR)", Inverse Probl. Sci. Eng., 26(8), 1198-1213. https://doi.org/10.1080/17415977.2017.1386188
  15. Lei, J., Nogal, M., Lozano-Galant, J.A., Xu, D. and Turmo, J. (2018), "Constrained observability method in static structural system identification", Struct. Control Health Monitor., 25(1), 2040e. https://doi.org/10.1002/stc.2040
  16. Lei, J., Lozano-Galant, J.A., Xu, D. and Turmo, J. (2019), "Structural system identification by measurement errorminimizing observability method", Struct. Control Health Monitor., 26(10), e2425. https://doi.org/10.1002/stc.2425
  17. Li, D.-S., Li, H.-N. and Fritzen, C.-P. (2012), "Load dependent sensor placement method: Theory and experimental validation", Mech. Syst. Signal. Pr., 31, 217-227. https://doi.org/10.1016/j.ymssp.2012.04.014
  18. Li, S., Dong, J., Lu, W., Li, H., Xu, W. and Jin, Y. (2017), "Optimal sensor placement for cable force monitoring using spatial correlation analysis and bond energy algorithm", Smart Struct. Syst., Int. J., 20(6), 769-780. https://doi.org/10.12989/sss.2017.20.6.769
  19. Liu, C., Teng, J. and Peng, Z. (2020), "Optimal sensor placement for bridge damage detection using deflection influence line", Smart Struct. Syst., Int. J., 25(2), 169-181. https://doi.org/10.12989/sss.2020.25.2.169
  20. Lozano-Galant, J.A., Nogal, M., Castillo, E. and Turmo, J. (2013), "Application of observability techniques to structural system identification", Comput.-Aided. Civil Infrastr., 28(6), 434-450. https://doi.org/10.1111/mice.12004
  21. Lozano-Galant, J.A., Nogal, M., Turmo, J. and Castillo, E. (2015), "Selection of measurement sets in static structural identification of bridges using observability trees", Comput. Concrete, Int. J., 15(5), 771-794. https://doi.org/10.12989/cac.2015.15.5.771
  22. Ma, Q. and Solis, M. (2018), "Damage localization and quantification in beams from slope discontinuities in static deflections", Smart Struct. Syst., Int. J., 22(3), 291-302. https://doi.org/10.12989/sss.2018.22.3.291
  23. Meo, M. and Zumpano, G. (2005), "On the optimal sensor placement techniques for a bridge structure", Eng. Struct., 27(10), 1488-1497. https://doi.org/10.1016/j.engstruct.2005.03.015
  24. Nguyen, V.H., Schommer, S., Maas, S. and Zurbes, A. (2016), "Static load testing with temperature compensation for structural health monitoring of bridges", Eng. Struct., 127, 700-718. https://doi.org/10.1016/j.engstruct.2016.09.018
  25. Nogal, M., Lozano-Galant, J.A., Turmo, J. and Castillo, E. (2015), "Numerical damage identification of structures by observability techniques based on static loading tests", Struct. Infrastruct. E., 12(9), 1216-1227. https://doi.org/10.1080/15732479.2015.1101143
  26. Papadopoulos, L. and Garcia, E. (1998), "Structural damage identification - A probabilistic approach", Astronaut. Aeronaut., 36(11), 2137-2145. https://doi.org/10.2514/2.318
  27. Peng, T., Nogal, M., Casas, J.R., Lozano-Galant, J.A. and Turmo, J. (2020), "Constrained observability techniques for structural system identification using modal analysis", J. Sound. Vib., 479, 115368. https://doi.org/10.1016/j.jsv.2020.115368
  28. Sanayei, M. and Chitra, J. (2002), "Sensor placement for parameter estimation of structures using fisher information matrix", Proceedings of the 7th International Conference on Applications of Advanced Technologies in Transportation (AATT), Boston Marriot, USA, August. https://doi.org/10.1061/40632(245)49
  29. Sanayei, M., Dicarlo, C.J., Rohela, P., Miller, E.L. and Kilmer, M.E. (2015), "Sensor placement using fisher information matrix for robust finite element model updating", Life Cycle Reliab. Saf. Eng., 4(2), 28-39.
  30. Sanayei, M. and Scampoli, S.F. (1991), "Structural element stiffness identification from static test data", J. Eng. Mech., 117(5), 1021-1036. https://doi.org/10.1061/(ASCE)0733-9399(1991)117:5(1021)
  31. Sheena, Z., Unger, A. and Zalmanovich, A. (1982), "Theoretical stiffness matrix correction by using static test results", Isr. J. Technol., 20, 245-253.
  32. Tan, Y. and Zhang, L. (2019), "Computational methodologies for optimal sensor placement in structural health monitoring: A review", Struct. Health. Monitor., 19(4), 1287-1308. https://doi.org/10.1177/1475921719877579
  33. Tomas, D., Lozano-Galant, J.A., Ramos, G. and Turmo, J. (2018), "Structural system identification of thin web bridges by observability techniques considering shear deformation", Thin. Wall. Struct., 123, 282-293. https://doi.org/10.1016/j.tws.2017.11.017
  34. Tong, K.H., Bakhary, N., Kueh, A.B.H.H., Mohd Yassin, A.Y., Yassin, A.Y.M., Bakhary, N. and Kueh, A.B.H.H. (2014), "Optimal sensor placement for mode shapes using improved simulated annealing", Smart Struct. Syst., Int. J., 13(3), 389-406. https://doi.org/10.12989/sss.2014.13.3.389
  35. Wang, X., Hu, N., Fukunaga, H. and Yao, Z.H. (2001), "Structural damage identification using static test data and changes in frequencies", Eng. Struct., 23(6), 610-621. https://doi.org/10.1016/S0141-0296(00)00086-9
  36. Wang, X., Yang, H., Wang, L. and Qiu, Z. (2012), "Interval analysis method for structural damage identification based on multiple load cases", J. Appl. Mech., 79(5), 051010. https://doi.org/10.1115/1.4006447
  37. Zhang, Y., Kim, C.W., Zhang, L., Bai, Y., Yang, H., Xu, X. and Zhang, Z. (2020), "Long term structural health monitoring for old deteriorated bridges: A Copula-ARMA approach", Smart Struct. Syst., Int. J., 25(3), 285-299. https://doi.org/10.12989/sss.2020.25.3.285
  38. Zhou, G.D., Yi, T.H., Zhang, H. and Li, H.N. (2015), "Optimal sensor placement under uncertainties using a nondirective movement glowworm swarm optimization algorithm", Smart Struct. Syst., Int. J., 16(2), 243-262. https://doi.org/10.12989/sss.2015.16.2.243