Smart Structures and Systems

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Feasibility study on an acceleration signal-based translational and rotational mode shape estimation approach utilizing the linear transformation matrix

  • Seung-Hun Sung (Agency for Defense Development) ;
  • Gil-Yong Lee (Department of Mechanical Engineering, KAIST) ;
  • In-Ho Kim (Department of Civil Engineering, Kunsan National University)
  • Received : 2022.12.22
  • Accepted : 2023.06.27
  • Published : 2023.07.25
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Abstract

In modal analysis, the mode shape reflects the vibration characteristics of the structure, and thus it is widely performed for finite element model updating and structural health monitoring. Generally, the acceleration-based mode shape is suitable to express the characteristics of structures for the translational vibration; however, it is difficult to represent the rotational mode at boundary conditions. A tilt sensor and gyroscope capable of measuring rotational mode are used to analyze the overall behavior of the structure, but extracting its mode shape is the major challenge under the small vibration always. Herein, we conducted a feasibility study on a multi-mode shape estimating approach utilizing a single physical quantity signal. The basic concept of the proposed method is to receive multi-metric dynamic responses from two sensors and obtain mode shapes through bridge loading test with relatively large deformation. In addition, the linear transformation matrix for estimating two mode shapes is derived, and the mode shape based on the gyro sensor data is obtained by acceleration response using ambient vibration. Because the structure's behavior with respect to translational and rotational mode can be confirmed, the proposed method can obtain the total response of the structure considering boundary conditions. To verify the feasibility of the proposed method, we pre-measured dynamic data acquired from five accelerometers and five gyro sensors in a lab-scale test considering bridge structures, and obtained a linear transformation matrix for estimating the multi-mode shapes. In addition, the mode shapes for two physical quantities could be extracted by using only the acceleration data. Finally, the mode shapes estimated by the proposed method were compared with the mode shapes obtained from the two sensors. This study confirmed the applicability of the multi-mode shape estimation approach for accurate damage assessment using multi-dimensional mode shapes of bridge structures, and can be used to evaluate the behavior of structures under ambient vibration.

Keywords

Acknowledgement

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2020R1I1A1A01073676) and was supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 21CTAP-C164155-01).

References

  1. Ali, A., Sandhu, T.Y. and Usman, M. (2019), "Ambient vibration testing of a pedestrian bridge using low-cost accelerometers for SHM applications", Smart Cities, 2(1), 20-30. https://doi.org/10.3390/smartcities2010002
  2. Bodeux, J.B. and Golinval, J.C. (2001), "Application of ARMAV models to the identification and damage detection of mechanical and civil engineering structures", Smart Mater. Struct., 10(3), 479. https://doi.org/10.1088/0964-1726/10/3/309
  3. Caicedo, J.M., Dyke, S.J. and Johnson, E.A. (2004), "Natural excitation technique and eigensystem realization algorithm for phase I of the IASC-ASCE benchmark problem: Simulated data", J. Eng. Mech., 130(1), 49-60. https://doi.org/10.1061/(ASCE)0733-9399(2004)130:1(49)
  4. Chang, M. and Pakzad, S.N. (2013), "Modified natural excitation technique for stochastic modal identification", J. Struct. Eng., 139(10), 1753-1762. https://doi.org/10.1061/(ASCE)ST.1943-541X.0000559
  5. Chiang, D.-Y. and Lin, C.-S. (2010), "Identification of modal parameters from ambient vibration data using eigensystem realization algorithm with correlation technique", J. Mech. Sci. Technol., 24(12), 2377-2382. https://doi.org/10.1007/s12206-010-1005-0
  6. Farrar, C. and James Iii, G. (1997), "System identification from ambient vibration measurements on a bridge", J. Sound Vib., 205(1), 1-18. https://doi.org/10.1006/jsvi.1997.0977
  7. Felber, A.J. (1994), Development of a hybrid bridge evaluation system, University of British Columbia
  8. Juang, J.-N. (1994), Applied system identification, Prentice-Hall, Inc.
  9. Kaloop, M., Elsharawy, M., Salah, B., Hu, J. and Kim, D. (2020), "Performance assessment of bridges using short-period structural health monitoring system: Sungsu bridge case study", Smart Struct. Syst., Int. J., 26(5), 605-617. https://doi.org/10.12989/sss.2020.26.5.667
  10. 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
  11. Ljung, L. (1987), "Theory for the user", System Identification.
  12. Moore, E.H. (1920), "On the reciprocal of the general algebraic matrix", Bull. Am. Math. Soc., 26, 394-395.
  13. Park, K., Kim, S. and Torbol, M. (2016), "Operational modal analysis of reinforced concrete bridges using autoregressive model", Smart Struct. Syst., Int. J., 17(6), 1017-1030. https://doi.org/10.12989/sss.2016.17.6.1017
  14. Qu, C.-X., Yi, T.-H., Yang, X.-M. and Li, H.-N. (2017), "Spurious mode distinguish by eigensystem realization algorithm with improved stabilization diagram", Struct. Eng. Mech., Int. J., 63(6), 743-750. https://doi.org/10.12989/sem.2017.63.6.743
  15. Qu, C.X., Yi, T.H. and Li, H.N. (2019), "Mode identification by eigensystem realization algorithm through virtual frequency response function", Struct. Control Health Monitor., 26(10), e2429. https://doi.org/10.1002/stc.2429
  16. Sim, S.-H., Spencer Jr, B. and Nagayama, T. (2011), "Multimetric sensing for structural damage detection", J. Eng. Mech., 137(1), 22-30. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000199
  17. Sung, S.-H., Park, J.-W., Nagayama, T. and Jung, H.-J. (2013), "A multi-scale sensing and diagnosis system combining accelerometers and gyroscopes for bridge health monitoring", Smart Mater. Struct., 23(1), 015005. https://doi.org/10.1088/0964-1726/23/1/015005
  18. Yang, J., Sun, Y., Jing, H. and Li, P. (2023), "An improved NExT method for modal identification with tests validation", Eng. Struct., 274, 115192. https://doi.org/10.1016/j.engstruct.2022.115192