Smart Structures and Systems

  • 제14권4호
  • /
  • Pages.699-718
  • /
  • 2014
  • /
  • 1738-1584(pISSN)
  • /
  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Extension of indirect displacement estimation method using acceleration and strain to various types of beam structures

  • Cho, Soojin (School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology) ;
  • Sim, Sung-Han (School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology) ;
  • Park, Jong-Woong (Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign) ;
  • Lee, Junhwa (School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
  • 투고 : 2014.05.30
  • 심사 : 2014.08.30
  • 발행 : 2014.10.25
⟨ 이전 논문 다음 논문 ⟩

초록

The indirect displacement estimation using acceleration and strain (IDEAS) method is extended to various types of beam structures beyond the previous validation on the prismatic or near-prismatic beams. By fusing different types of responses, the IDEAS method is able to estimate displacements containing pseudo-static components with high frequency noise to be significantly reduced. However, the concerns to the IDEAS method come from possible disagreement of the assumed sinusoidal mode shapes to the actual mode shapes, which allows the IDEAS method to be valid only for simply-supported prismatic beams and limits its applicability to real world problems. In this paper, the extension of the IDEAS method to the general types of beams is investigated by the mathematical formulation of the modal mapping matrix only for the monitored substructure, so-called monitoring span. The formulation particularly considers continuous and wide beams to extend the IDEAS method to general beam structures that reflect many real bridges. Numerical simulations using four types of beams with various irregularities are presented to show the effectiveness and accuracy of the IDEAS method in estimating displacements.

키워드

참고문헌

  1. Altunisik, A.C., Bayraktar, A. and Ozdemir, H. (2012), "Seismic safety assessment of eynel highway steel bridge using ambient vibration measurements", Smart Struct. Syst., 10(2), 131-154. https://doi.org/10.12989/sss.2012.10.2.131
  2. Atkinson, K.E. (2008), An introduction to numerical analysis, John Wiley & Sons.
  3. Bani-Hani, K.A., Zibdeh, H.S. and Hamdaoui, K. (2008), "Health monitoring of a historical monument in Jordan based on ambient vibration test", Smart Struct. Syst., 4(2), 195-208. https://doi.org/10.12989/sss.2008.4.2.195
  4. Doebling, S.W., Farrar, C.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
  5. Foss, G. and Haugse, E. (1995), "Using modal test results to develop strain to displacement transformations", Proceedings of the 13th Int. Modal Analysis Conf.
  6. Gindy, M., Nassif, H.H. and Velde, J. (2008), "Bridge displacement estimates from measured acceleration records", Transport. Res. Rec., 2028, 136-145.
  7. Jung, B.S., Kim, N.S. and Koon, S.K. (2006), "Estimation of displacement responses using the wavelet decomposition signal", J. Korea Concrete Inst., 18(3), 347-354. https://doi.org/10.4334/JKCI.2006.18.3.347
  8. Kandula, V., DeBrunner, L., DeBrunner, V. and Rambo-Roddenberry, M. (2012), "Field testing of indirect displacement estimation using accelerometers", Proceedings of the Conf. Record of the 46th Asilomar Conf. Signals, Systems, and Computers.
  9. Kang, L.H., Kim, D.K. and Han, J.H. (2007), "Estimation of dynamic structural displacements using fiber Bragg grating strain sensors", J. Sound Vib., 305(3), 534-542. https://doi.org/10.1016/j.jsv.2007.04.037
  10. Koo, K.Y., Sung, S.H., Park, J.W. and Jung, H.J. (2010), "Damage detection of shear buildings using deflections obtained by modal flexibility", Smart Mat. Struct., 19(11), 115026. https://doi.org/10.1088/0964-1726/19/11/115026
  11. Kim, J.T., Ho, D.D., Nguyen, K.D., Hong, D.S., Shin, S.W., Yun, C.B. and Shinozuka, M. (2013), "System identification of a cable-stayed bridge using vibration responses measured by a wireless sensor network", Smart Struct. Syst., 11(5), 533-553. https://doi.org/10.12989/sss.2013.11.5.533
  12. Lee, J.J. and Shinozuka, M. (2006), "A vision-based system for remote sensing of bridge displacement", NDT & E Int., 39(5), 425-431. https://doi.org/10.1016/j.ndteint.2005.12.003
  13. Lee, H.S., Hong, Y.H. and Park, H.W. (2010), "Design of an FIR filter for the displacement reconstruction using measured acceleration in low-frequency dominant structures", Int. J. Numer. Meth. Eng., 82(4), 403-434.
  14. Ma, T.W., Bell, M., Lu, W. and Xu, N.S. (2014), "Recovering structural displacements and velocities from acceleration", Smart Struct. Syst., 14(2), 191-207. https://doi.org/10.12989/sss.2014.14.2.191
  15. Nassif, H.H., Gindy, M. and Davis, J.(2005), "Comparison of laser Doppler vibrometer with contact sensors for monitoring bridge deflection and vibration", NDT & E Int., 38(3), 213-218. https://doi.org/10.1016/j.ndteint.2004.06.012
  16. Park, J.W., Sim, S.H. and Jung, H.J. (2013), "Displacement estimation using multimetric data fusion", IEEE/ASME Trans. Mechatronics, 18(6), DOI: 10.1109/TMECH.2013.2275187.
  17. Park, K.T., Kim, S.H., Park, H.S. and Lee, K.W. (2005), "The determination of bridge displacement using measured acceleration", Eng. Struct., 27(3), 371-378. https://doi.org/10.1016/j.engstruct.2004.10.013
  18. Ribeiro, J.G.T., Freire, J.L.F. and Castro, J.T.P. (1997), "Problems in analogue double integration to determine displacements from acceleration data", Proceedings of the 15th Int. Modal Anal. Conf.
  19. Shin, S., Lee, S.U. and Kim, N.S. (2012), "Estimation of bridge displacement responses using FBG sensors and theoretical mode shapes," Struct. Eng. Mech., 42(2), 229-245. https://doi.org/10.12989/sem.2012.42.2.229
  20. Ye, X.W., Ni, Y.Q., Wai, T.T., Wong, K.Y., Zhang, X.M. and Xu, F. (2013)."A vision-based system for dynamic displacement measurement of long-span bridges: algorithm and verification", Smart Struct. Syst., 12(3-4), 363-379. https://doi.org/10.12989/sss.2013.12.3_4.363
  21. Yi, J.H., Cho, S., Koo, K.Y., Yun, C.B., Kim, J.T., Lee, C.G. and Lee, W.T. (2007), "Structural performance evaluation of a steel-plate girder bridge using ambient acceleration measurements", Smart Struct. Syst., 3(3), 281-298. https://doi.org/10.12989/sss.2007.3.3.281
  22. Zhou, C., Li, H.N., Li, D.S., Lin, Y.X. and Yi, T.H. (2013), "Online damage detection using pair cointegration method of time-varying displacement", Smart Struct. Syst., 12(3-4), 309-325. https://doi.org/10.12989/sss.2013.12.3_4.309

피인용 문헌

  1. Computer Vision-Based Structural Displacement Measurement Robust to Light-Induced Image Degradation for In-Service Bridges vol.17, pp.10, 2017, https://doi.org/10.3390/s17102317
  2. SOUNDNESS EVALUATION OF BRIDGE BEARING BASED ON TWO PLACES DISPLACEMENT MEASUREMENT USING MEMS ACCELEROMETERS vol.73, pp.2, 2017, https://doi.org/10.2208/jscejam.73.I_649
  3. Determination Method of Bridge Rotation Angle Response Using MEMS IMU vol.16, pp.12, 2016, https://doi.org/10.3390/s16111882
  4. Estimation of flexibility matrix of beam structures using multisensor fusion vol.1, pp.2, 2016, https://doi.org/10.1080/24705314.2016.1179494
  5. Displacement estimation of bridge structures using data fusion of acceleration and strain measurement incorporating finite element model vol.15, pp.3, 2015, https://doi.org/10.12989/sss.2015.15.3.645
  6. Technique for Determining Bridge Displacement Response Using MEMS Accelerometers vol.16, pp.12, 2016, https://doi.org/10.3390/s16020257
  7. Traffic Safety Evaluation for Railway Bridges Using Expanded Multisensor Data Fusion vol.31, pp.10, 2016, https://doi.org/10.1111/mice.12210
  8. Multi-point displacement monitoring of bridges using a vision-based approach vol.20, pp.2, 2015, https://doi.org/10.12989/was.2015.20.2.315
  9. Structural damage identification via response reconstruction under unknown excitation vol.24, pp.8, 2017, https://doi.org/10.1002/stc.1953
  10. Real-time monitoring system for local storage and data transmission by remote control vol.112, 2017, https://doi.org/10.1016/j.advengsoft.2017.06.010
  11. Reconstruction of Unmeasured Strain Responses in Bottom-fixed Offshore Structures by Multimetric Sensor Data Fusion vol.188, 2017, https://doi.org/10.1016/j.proeng.2017.04.461
  12. Visualization system for bridge deformations under live load based on multipoint simultaneous measurements of displacement and rotational response using MEMS sensors vol.146, 2017, https://doi.org/10.1016/j.engstruct.2017.05.036
  13. Experimental validation of Kalman filter-based strain estimation in structures subjected to non-zero mean input vol.15, pp.2, 2015, https://doi.org/10.12989/sss.2015.15.2.489
  14. Issues in structural health monitoring for fixed-type offshore structures under harsh tidal environments vol.15, pp.2, 2015, https://doi.org/10.12989/sss.2015.15.2.335
  15. Reference-Free Displacement Estimation of Bridges Using Kalman Filter-Based Multimetric Data Fusion vol.2016, 2016, https://doi.org/10.1155/2016/3791856
  16. Validation of a Data-fusion Based Solution in view of the Real-Time Monitoring of Cable-Stayed Bridges vol.199, 2017, https://doi.org/10.1016/j.proeng.2017.09.279
  17. Long-Term Deflection Prediction from Computer Vision-Measured Data History for High-Speed Railway Bridges vol.18, pp.5, 2018, https://doi.org/10.3390/s18051488
  18. Reliability Assessment of Deflection Limit State of a Simply Supported Bridge using vibration data and Dynamic Bayesian Network Inference vol.19, pp.4, 2019, https://doi.org/10.3390/s19040837
  19. Quasi real-time and continuous non-stationary strain estimation in bottom-fixed offshore structures by multimetric data fusion vol.23, pp.1, 2019, https://doi.org/10.12989/sss.2019.23.1.061
  20. Field Verification over One Year of a Portable Bridge Weigh-in-Motion System for Steel Bridges vol.24, pp.7, 2014, https://doi.org/10.1061/(asce)be.1943-5592.0001411
  21. Bridge Displacement Estimation Using a Co-Located Acceleration and Strain vol.20, pp.4, 2014, https://doi.org/10.3390/s20041109
  22. Long-term displacement measurement of full-scale bridges using camera ego-motion compensation vol.140, pp.None, 2014, https://doi.org/10.1016/j.ymssp.2020.106651
  23. Structural sensing with deep learning: Strain estimation from acceleration data for fatigue assessment vol.35, pp.12, 2014, https://doi.org/10.1111/mice.12565
  24. Estimation of Structural Deformed Configuration for Bridges Using Multi-Response Measurement Data vol.11, pp.9, 2014, https://doi.org/10.3390/app11094000