Smart Structures and Systems

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Dynamic deflection monitoring of high-speed railway bridges with the optimal inclinometer sensor placement

  • Li, Shunlong (School of Transportation Science and Engineering, Harbin Institute of Technology) ;
  • Wang, Xin (School of Transportation Science and Engineering, Harbin Institute of Technology) ;
  • Liu, Hongzhan (China Railway Design Corporation) ;
  • Zhuo, Yi (China Railway Design Corporation) ;
  • Su, Wei (China Railway Design Corporation) ;
  • Di, Hao (China Railway Design Corporation)
  • Received : 2019.12.25
  • Accepted : 2020.07.27
  • Published : 2020.11.25
⟨ Previous Next ⟩

Abstract

Dynamic deflection monitoring is an essential and critical part of structural health monitoring for high-speed railway bridges. Two critical problems need to be addressed when using inclinometer sensors for such applications. These include constructing a general representation model of inclination-deflection and addressing the ill-posed inverse problem to obtain the accurate dynamic deflection. This paper provides a dynamic deflection monitoring method with the placement of optimal inclinometer sensors for high-speed railway bridges. The deflection shapes are reconstructed using the inclination-deflection transformation model based on the differential relationship between the inclination and displacement mode shape matrix. The proposed optimal sensor configuration can be used to select inclination-deflection transformation models that meet the required accuracy and stability from all possible sensor locations. In this study, the condition number and information entropy are employed to measure the ill-condition of the selected mode shape matrix and evaluate the prediction performance of different sensor configurations. The particle swarm optimization algorithm, genetic algorithm, and artificial fish swarm algorithm are used to optimize the sensor position placement. Numerical simulation and experimental validation results of a 5-span high-speed railway bridge show that the reconstructed deflection shapes agree well with those of the real bridge.

Keywords

Acknowledgement

Financial support for this study was provided by the National Key R&D Program of China [2018YFB1600200], NSFC [51922034, 51678204 and 51638007], Heilongjiang Natural Science Foundation for Excellent Young Scholars [YQ2019E025] and Guangxi Science Base and Talent Program [710281886032].

References

  1. Azarbayejani, M., El-Osery, A.I., Choi, K.K. and Taha, M.M.R. (2008), "A probabilistic approach for optimal sensor allocation in structural health monitoring", Smart Mater. Struct., 17(5). https://doi.org/10.1088/0964-1726/17/5/055019.
  2. Beck, J.L. and Katafygiotis, L.S. (1998), "Updating models and their uncertainties. I: Bayesian statistical framework", J. Eng. Mech., 124(4), 455-461. https://doi.org/10.1061/(ASCE)0733-9399(1998)124:4(455).
  3. Bogert, P., Haugse, E. and Gehrki, R. (2003), "Structural shape identification from experimental strains using a modal transformation technique", Proceedings of the 44th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, Virginia, USA, April.
  4. Cha, Y.J., Agrawal, A.K., Kim, Y. and Raich, A.M. (2012), "Multiobjective genetic algorithms for cost-effective distributions of actuators and sensors in large structures", Expert Syst. Appl., 39(9), 7822-7833. https://doi.org/10.1016/j.eswa.2012.01.070.
  5. Downey, A., Hu, C. and Laflamme, S. (2018), "Optimal sensor placement within a hybrid dense sensor network using an adaptive genetic algorithm with learning gene pool", Struct. Health Monit., 17(3), 450-460. https://doi.org/10.1177/1475921717702537.
  6. Flynn, E.B. and Todd, M.D. (2010), "A Bayesian approach to optimal sensor placement for structural health monitoring with application to active sensing", Mech. Syst. Signal Process., 24(4), 891-903. https://doi.org/10.1016/j.ymssp.2009.09.003.
  7. Foss, G.C. and Haugse, E.D. (1995), "Using modal test results to develop strain to displacement transformations", Proceedings of the 13th International Modal Analysis Conference, Nashville, USA, February.
  8. Goswami, S., Ghosh, S. and Chakraborty, S. (2016), "Reliability analysis of structures by iterative improved response surface method", Struct. Safety., 60, 56-66. https://doi.org/10.1016/j.strusafe.2016.02.002.
  9. He, C., Xing, J.C., Li, J.L., Yang, Q.L., Wang, R.H. and Zhang, X. (2013), "A combined optimal sensor placement strategy for the structural health monitoring of bridge structures", Int. J. Distrib. Sens. Netw., 9(11), 820694. https://doi.org/10.1155/2013/820694.
  10. He, L.J., Lian, J.J., Ma, B. and Wang, H.J. (2014a), "Optimal multiaxial sensor placement for modal identification of large structures", Struct. Control Health Monit., 21(1), 61-79. https://doi.org/10.1002/stc.1550.
  11. He, X.L., Yang, X.S. and Zhao, L.Z. (2014b), "New method for high-speed railway bridge dynamic deflection measurement", J. Bridge Eng., 19(7), 05014004. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000612.
  12. Heo, G., Wang, M.L. and Satpathi, D. (1997), "Optimal transducer placement for health monitoring of long span bridge", Soil Dyn. Earthq. Eng.,16(7-8), 495-502. https://doi.org/10.1016/S0267-7261(97)00010-9.
  13. Hernandez, E.M. (2017), "Efficient sensor placement for state estimation in structural dynamics", Mech. Syst. Signal Process., 85, 789-800. https://doi.org/10.1016/j.ymssp.2016.09.005.
  14. Hou, X.M., Yang, X.S. and Huang, Q. (2005), "Using inclinometers to measure bridge deflection", J. Bridge Eng., 10(5), 564-569. https://doi.org/10.1061/(ASCE)1084-0702(2005)10:5(564).
  15. Kammer, D.C. (1996), "Optimal sensor placement for modal identification using system-realization methods", J. Guid. Control Dyn., 19(3), 729-731. https://doi.org/10.2514/3.21688.
  16. 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.
  17. Kim, H.I., Kang, L H. and Han, J.H. (2011), "Shape estimation with distributed fiber Bragg grating sensors for rotating structures", Smart Mater. Struct., 20(3), 035011. https://doi.org/10.1088/0964-1726/20/3/035011.
  18. Lei, Y., Liu, C., Jiang, Y.Q. and Mao, Y.K. (2013), "Substructure based structural damage detection with limited input and output measurements", Smart Struct. Syst., Int. J., 12(6), 619-640. http://dx.doi.org/10.12989/sss.2013.12.6.619.
  19. Li, B.B. and Kiureghian, A.D. (2016), "Robust optimal sensor placement for operational modal analysis based on maximum expected utility", Mech. Syst. Signal Process., 75, 155-175. https://doi.org/10.1016/j.ymssp.2016.01.005.
  20. Li, C.J. and Ulsoy, A.G. (1999), "High-precision measurement of tool-tip displacement using strain gauges in precision flexible line boring", Mech. Syst. Signal Process., 13(4), 531-546. https://doi.org/10.1006/mssp.1999.1223.
  21. Lian, J.J., He, L.J., Ma, B., Li, H.K. and Peng, W.X. (2013), "Optimal sensor placement for large structures using the nearest neighbour index and a hybrid swarm intelligence algorithm", Smart Mater. Struct., 22(9), 095015. https://doi.org/10.1088/0964-1726/22/9/095015.
  22. Lin, J.F., Xu, Y.L. and Law, S.S. (2018), "Structural damage detection-oriented multi-type sensor placement with multiobjective optimization", J. Sound Vib., 422, 568-589. https://doi.org/10.1016/j.jsv.2018.01.047.
  23. Liu, Y., Tao, Z.P., Yang, J. and Mao, F. (2019), "The modified artificial fish swarm algorithm for least-cost planning of a regional water supply network problem", Sustainability, 11(15), 4121. https://doi.org/10.3390/su11154121.
  24. Lu, W., Wen, R.F., Teng, J., Li, X.L. and Li, C. (2016), "Data correlation analysis for optimal sensor placement using a bond energy algorithm", Measurement, 91, 509-518. https://doi.org/10.1016/j.measurement.2016.05.089.
  25. Papadimitriou, C. (2002), "Applications of genetic algorithms in structural health monitoring", Proceedings of the 5th World Congress on Computational Mechanics, Vienna, Austria, August.
  26. Papadimitriou, C. (2004), "Optimal sensor placement methodology for parametric identification of structural systems", J. Sound Vib., 278(4-5), 923-947. https://doi.org/10.1016/j.jsv.2003.10.063.
  27. Papadimitriou, C., Beck, J.L. and Au, S.K. (2000), "Entropy-based optimal sensor location for structural model updating", J. Vib. Control., 6(5), 781-800. https://doi.org/10.1177/107754630000600508.
  28. Pei, X.Y., Yi, T.H. and Li, H.N. (2018), "A multitype sensor placement method for the modal estimation of structure", Smart Struct. Syst., Int. J., 21(4), 407-420. http://dx.doi.org/10.12989/sss.2018.21.4.407.
  29. Pisoni, A.C., Santolini, C., Hauf, D.E. and Dubowsky, S. (1995), "Displacements in a vibrating body by strain gage measurements", Proceedings of the SPIE the International Society for Optical Engineering, Fano, Italy, July.
  30. Rapp, S., Kang, L.H., Han, J.H., Mueller, U.C. and Baier, H. (2009), "Displacement field estimation for a two-dimensional structure using fiber Bragg grating sensors", Smart Mater. Struct., 18(2), 025006. https://doi.org/10.1088/0964-1726/18/2/025006.
  31. Sousa, H., Cavadas, F., Henriques, A., Bento, J. and Figueiras, J. (2013), "Bridge deflection evaluation using strain and rotation measurements", Smart Struct. Syst., Int. J., 11(4), 365-386. http://dx.doi.org/10.12989/sss.2013.11.4.365.
  32. Trendafilova, I., Heylen, W. and Van Brussel, H. (2001), "Measurement point selection in damage detection using the mutual information concept", Smart Mater. Struct., 10(3), 528-533. https://doi.org/10.1088/0964-1726/10/3/315.
  33. Udwadia, F.E. (1994), "Methodology for optimum sensor locations for parameter-identification in dynamic-systems", J. Eng. Mech., 120(2), 368-390. https://doi.org/10.1061/(ASCE)0733-9399(1994)120:2(368).
  34. Wang, J., Law, S.S. and Yang, Q.S. (2014), "Sensor placement method for dynamic response reconstruction", J. Sound Vib., 333(9), 2469-2482. https://doi.org/10.1016/j.jsv.2013.12.014.
  35. Xiong, H.B., Cao, J.X. and Zhang, F.L. (2018), "Inclinometerbased method to monitor displacement of high-rise buildings", Struct. Monit. Maint., Int. J., 5(1), 111-127. https://doi.org/10.12989/smm.2018.5.1.111.
  36. Xu, H., Zhao, Y.Q., Ye, C. and Lin, F. (2019), "Integrated optimization for mechanical elastic wheel and suspension based on an improved artificial fish swarm algorithm", Adv. Eng. Softw., 137, 102722. https://doi.org/10.1016/j.advengsoft.2019.102722.
  37. Yang, C., Zhang, X.P., Huang, X.Q., Cheng, Z.A., Zhang, X.H. and Hou, X.B. (2017a), "Optimal sensor placement for deployable antenna module health monitoring in SSPS using genetic algorithm", Acta Astronaut., 140, 213-224. https://doi.org/10.1016/j.actaastro.2017.08.025.
  38. Yang, Y., Dorn, C., Mancini, T., Talken, Z., Kenyon, G., Farrar, C. and Mascarenas, D. (2017b), "Blind identification of full-field vibration modes from video measurements with phase-based video motion magnification", Mech. Syst. Signal Process., 85, 567-590. https://doi.org/10.1016/j.ymssp.2016.08.041.
  39. Yang, Y., Jung, H.K., Dorn, C., Park, G., Farrar, C. and Mascarenas, D. (2019), "Estimation of full‐field dynamic strains from digital video measurements of output‐only beam structures by video motion processing and modal superposition", Struct. Control Health Monit., 26(10), 2408. https://doi.org/10.1002/stc.2408.
  40. Yi, T.H., Li, H.N. and Gu, M. (2012), "Sensor placement for structural health monitoring of Canton tower", Smart Struct. Syst., Int. J., 10(4-5), 313-329. https://doi.org/10.12989/sss.2012.10.4_5.313.
  41. Yi, T.H., Li, H.N. and Zhang, X.D. (2015), "Sensor placement optimization in structural health monitoring using distributed monkey algorithm", Smart Struct. Syst., Int. J., 15(1), 191-207. https://doi.org/10.12989/sss.2015.15.1.191.
  42. Zhang, C.D. and Xu, Y.L. (2016), "Optimal multi-type sensor placement for response and excitation reconstruction", J. Sound Vib., 360, 112-128. https://doi.org/10.1016/j.jsv.2015.09.018.
  43. Zhang, X.H., Zhu, S., Xu, Y.L. and Homg, X.J. (2011), "Integrated optimal placement of displacement transducers and strain gauges for better estimation of structural response", Int. J. Struct. Stab. Dyn., 11(3), 581-602. https://doi.org/10.1142/S0219455411004221.
  44. Zhang, X.H., Xu, Y.L., Zhu, S.Y. and Zhan, S. (2014a), "Dual-type sensor placement for multi-scale response reconstruction", Mechatronics, 24(4), 376-384. https://doi.org/10.1016/j.mechatronics.2013.05.007.
  45. Zhang, X., Li, J.L., Xing, J.C., Wang, P., Yang, Q.L., Wang, R.H. and He, C. (2014b), "Optimal sensor placement for latticed shell structure based on an improved particle swarm optimization algorithm", Math. Probl. Eng., 2014, 1-12. https://doi.org/10.1155/2014/743904.
  46. 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.
  47. Zhou, J.Z., Cai, Z., Kang, L., Tang, B.F. and Xu, W.H. (2019), "Deformation sensing and electrical compensation of smart skin antenna structure with optimal fiber Bragg grating strain sensor placements", Compos. Struct., 211, 418-432. https://doi.org/10.1016/j.compstruct.2018.12.048.