Smart Structures and Systems

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Indirect displacement monitoring of high-speed railway box girders consider bending and torsion coupling effects

  • Wang, Xin (School of Transportation Science and Engineering, Harbin Institute of Technology) ;
  • Li, Zhonglong (School of Transportation Science and Engineering, Harbin Institute of Technology) ;
  • Zhuo, Yi (China Railway Design Corporation) ;
  • Di, Hao (China Railway Design Corporation) ;
  • Wei, Jianfeng (China Railway Design Corporation) ;
  • Li, Yuchen (School of Transportation Science and Engineering, Harbin Institute of Technology) ;
  • Li, Shunlong (School of Transportation Science and Engineering, Harbin Institute of Technology)
  • Received : 2021.06.01
  • Accepted : 2021.09.17
  • Published : 2021.12.25
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Abstract

The dynamic displacement is considered to be an important indicator of structural safety, and becomes an indispensable part of Structural Health Monitoring (SHM) system for high-speed railway bridges. This paper proposes an indirect strain based dynamic displacement reconstruction methodology for high-speed railway box girders. For the typical box girders under eccentric train load, the plane section assumption and elementary beam theory is no longer applicable due to the bend-torsion coupling effects. The monitored strain was decoupled into bend and torsion induced strain, pre-trained multi-output support vector regression (M-SVR) model was employed for such decoupling process considering the sensor layout cost and reconstruction accuracy. The decoupled strained based displacement could be reconstructed respectively using box girder plate element analysis and mode superposition principle. For the transformation modal matrix has a significant impact on the reconstructed displacement accuracy, the modal order would be optimized using particle swarm algorithm (PSO), aiming to minimize the ill conditioned degree of transformation modal matrix and the displacement reconstruction error. Numerical simulation and dynamic load testing results show that the reconstructed displacement was in good agreement with the simulated or measured results, which verifies the validity and accuracy of the algorithm proposed in this paper.

Keywords

Acknowledgement

Financial support for this study was provided by NSFC [51922034, 51678204, and 51638007], Heilongjiang Natural Science Foundation for Excellent Young Scholars [YQ2019E025], and China Railway Design Corporation R&D Program [2020YY240603, 2020YY340619].

References

  1. Barthorpe, R.J. and Worden, K. (2020), "Emerging trends in optimal structural health monitoring system design: From sensor placement to system evaluation", J. Sensor Actuator Networks, 9(3). https://doi.org/10.3390/jsan9030031
  2. Bernardini, G., Porcelli, R., Serafini, J. and Masarati, P. (2018), "Rotor blade shape reconstruction from strain measurements", Aerosp. Sci. Technol., 79, 580-587. https://doi.org/10.1016/j.ast.2018.06.012
  3. Cerracchio, P., Gherlone, M., Di Sciuva, M. and Tessler, A. (2015a), "A novel approach for displacement and stress monitoring of sandwich structures based on the inverse finite element method", Compos. Struct., 127, 69-76. https://doi.org/10.1016/j.compstruct.2015.02.081.
  4. Cerracchio, P., Gherlone, M. and Tessler, A. (2015b), "Real-time displacement monitoring of a composite stiffened panel subjected to mechanical and thermal loads", Meccanica, 50(10), 2487-2496. https://doi.org/10.1007/s11012-015-0146-8
  5. Das, S. and Dhang, N. (2020), "Structural damage identification of truss structures using self-controlled multi-stage particle swarm optimization", Smart Struct. Syst., Int. J., 25(3), 345-368. https://doi.org/10.12989/sss.2020.25.3.345
  6. Di, H.T. (2012), "Space curve fitting method based on fiber-optic curvature gages", Optics Laser Technol., 44(1), 290-294. https://doi.org/10.1016/j.optlastec.2011.07.007
  7. Esposito, M. and Gherlone, M. (2020), "Composite wing box deformed-shape reconstruction based on measured strains: Optimization and comparison of existing approaches", Aerosp. Sci. Technol., 99, 105758. https://doi.org/10.1016/j.ast.2020.105758
  8. Ferreira, P., Caetano, E., Ramos, L. and Pinto, P. (2017), "Shape sensing monitoring system based on fiber-optic strain measurements: Laboratory tests", Experim. Techniq., 41(4), 407-420. https://doi.org/10.1007/s40799-017-0187-0
  9. Fesharaki, J.J. and Golabi, S. (2016), "A novel method to specify pattern recognition of actuators for stress reduction based on Particle swarm optimization method", Smart Struct. Syst., Int. J., 17(5), 725-742. https://doi.org/10.12989/sss.2016.17.5.725
  10. Garg, P., Moreu, F., Ozdagli, A., Reda Taha, M. and Mascarenas, D. (2019), "Noncontact dynamic displacement measurement of structures using a moving laser Doppler vibrometer", J. Bridge Eng., 24(9), 04019089. https://doi.org/10.1061/(asce)be.1943-5592.0001472.
  11. Gherlone, M., Cerracchio, P., Mattone, M., Di Sciuva, M. and Tessler, A. (2014), "An inverse finite element method for beam shape sensing: theoretical framework and experimental validation", Smart Mater. Struct., 23(4), 045027. https://doi.org/10.1088/0964-1726/23/4/045027
  12. Jalsan, K.E., Soman, R.N., Flouri, K., Kyriakides, M.A., Feltrin, G. and Onoufriou, T. (2014), "Layout optimization of wireless sensor networks for structural health monitoring", Smart Struct. Syst., Int. J., 14(1), 39-54. https://doi.org/10.12989/sss.2014.14.1.039
  13. Javdani, S., Fabian, M., Ams, M., Carlton, J., Sun, T. and Grattan, K.T.V. (2014), "Fiber Bragg grating-based system for 2-D analysis of vibrational modes of a steel propeller blade", J. Lightwave Technol., 32(23), 3991-3997. https://doi.org/10.1109/jlt.2014.2361631
  14. Jones, R.T., Bellemore, D.G., Berkoff, T.A., Sirkis, J.S., Davis, M.A., Putnam, M.A., Friebele, E.J. and Kersey, A.D. (1998), "Determination of cantilever plate shapes using wavelength division multiplexed fiber Bragg grating sensors and a least-squares strain-fitting algorithm", Smart Mater. Struct., 7(2), 178-188. https://doi.org/10.1088/0964-1726/7/2/005
  15. Kaloop, M.R., Elbeltagi, E., Hu, J.W. and Elrefai, A. (2017), "Recent advances of structures monitoring and evaluation using GPS-time series monitoring systems: A review", ISPRS Int. J. Geo-Info., 6(12), 382. https://doi.org/10.3390/ijgi6120382
  16. Karimian, S.F. and Modarres, M. (2021), "Acoustic emission signal clustering in CFRP laminates using a new feature set based on waveform analysis and information entropy analysis", Compos. Struct., 268, 113987. https://doi.org/10.1016/j.compstruct.2021.113987
  17. Kefal, A. and Oterkus, E. (2016a), "Displacement and stress monitoring of a chemical tanker based on inverse finite element method", Ocean Eng., 112, 33-46. https://doi.org/10.1016/j.oceaneng.2015.11.032
  18. Kefal, A. and Oterkus, E. (2016b), "Displacement and stress monitoring of a Panamax containership using inverse finite element method", Ocean Eng., 119, 16-29. https:// doi.org/10.1016/j.oceaneng.2016.04.025
  19. Kefal, A. and Yildiz, M. (2017), "Modeling of sensor placement strategy for shape sensing and structural health monitoring of a wing-shaped sandwich panel using inverse finite element method", Sensors, 17(12), 2775. https://doi.org/10.3390/s1712775
  20. Kefal, A., Oterkus, E., Tessler, A. and Spangler, J.L. (2016), "A quadrilateral inverse-shell element with drilling degrees of freedom for shape sensing and structural health monitoring", Eng. Sci. Technol.-Int. J.-Jestech, 19(3), 1299-1313. https://doi.org/10.1016/j.jestch.2016.03.006
  21. Kefal, A., Tessler, A. and Oterkus, E. (2017), "An enhanced inverse finite element method for displacement and stress monitoring of multilayered composite and sandwich structures", Compos. Struct., 179, 514-540. https://doi.org/10.1016/j.compstruct.2017.07.078
  22. Kefal, A., Mayang, J.B., Oterkus, E. and Yildiz, M. (2018), "Three dimensional shape and stress monitoring of bulk carriers based on iFEM methodology", Ocean Eng., 147, 256-267. https://doi.org/10.1016/j.oceaneng.2017.10.040
  23. Kefal, A., Tabrizi, I.E., Yildiz, M. and Tessler, A. (2021), "A smoothed iFEM approach for efficient shape-sensing applications: Numerical and experimental validation on composite structures", Mech. Syst. Signal Process., 152, 107486. https://doi.org/10.1016/j.ymssp.2020.107486
  24. 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
  25. Kim, H.-I., Han, J.-H. and Bang, H.-J. (2014), "Real-time deformed shape estimation of a wind turbine blade using distributed fiber Bragg grating sensors", Wind Energy, 17(9), 1455-1467. https://doi.org/10.1002/we.1644
  26. Kristek, V. (1979), Theory of Box Girders, John Wiley and Sons, New York, NY, USA.
  27. Lee, J.J., Cho, S., Tae, L.W., Shinozuka, M., Yun, C.B. and Lee, C.-G. (2006), "Evaluation of bridge load carrying capacity based on dynamic displacement measurement using real-time image processing techniques", Steel Struct., 6(5), 377-385.
  28. Lee, J.J., Fukuda, Y., Shinozuka, M., Cho, S. and Yun, C.B. (2007), "Development and application of a vision-based displacement measurement system for structural health monitoring of civil structures", Smart Struct. Syst., Int. J., 3(3), 373-384. https://doi.org/10.12989/sss.2007.3.3.373
  29. 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
  30. Li, M., Kefal, A., Cerik, B.C. and Oterkus, E. (2020), "Dent damage identification in stiffened cylindrical structures using inverse Finite Element Method", Ocean Eng., 198, 106944. https://doi.org/10.1016/j.oceaneng.2020.106944
  31. Liu, M., Zhang, X., Song, H., Wang, J. and Zhou, S. (2018), "Reconstruction algorithm for obtaining the bending deformation of the base of heavy-duty machine tool using inverse Finite Element Method", Metrol. Measur. Syst., 25(4), 727-741. https://doi.org/10.24425/mms.2018.124878
  32. Luo, Z., Li, J., Hong, G. and Li, H. (2019), "Strain-based displacement field reconstruction method for thin rectangular plate through orthogonal deflection curves bridging", Struct. Control Health Monitor., 27(1), e2457. https://doi.org/10.1002/stc.2457
  33. Nestorovic, T., Trajkov, M. and Garmabi, S. (2015), "Optimal placement of piezoelectric actuators and sensors on a smart beam and a smart plate using multi-objective genetic algorithm", Smart Struct. Syst., Int. J., 15(4), 1041-1062. https://doi.org/10.12989/sss.2015.15.4.1041
  34. Papa, U., Russo, S., Lamboglia, A., Del Core, G. and Iannuzzo, G. (2017), "Health structure monitoring for the design of an innovative UAS fixed wing through inverse finite element method (iFEM)", Aerosp. Sci. Technol., 69, 439-448. https://doi.org/10.1016/j.ast.2017.07.005
  35. 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
  36. Perry, B.J. and Guo, Y. (2021), "A portable three-component displacement measurement technique using an unmanned aerial vehicle (UAV) and computer vision: A proof of concept", Measurement, 176, 109222. https://doi.org/10.1016/j.measurement.2021.109222
  37. Ribeiro, D., Calcada, R., Ferreira, J. and Martins, T. (2014), "Non-contact measurement of the dynamic displacement of railway bridges using an advanced video-based system", Eng. Struct., 75, 164-180. https://doi.org/10.1016/j.engstruct.2014. 04.051
  38. Roy, R., Gherlone, M. and Surace, C. (2020), "Damage localisation in thin plates using the inverse finite element method", Proceedings of the 13th International Conference on Damage Assessment of Structures, Porto, Portugal, July.
  39. Sanchez-Fernadez, M., de-Prado-Cumplido, M., Arenas-Garcia, J. and Perez-Cruz, F. (2004), "SVM multiregression for nonlinear channel estimation in multiple-input multiple-output systems", IEEE Transact. Signal Process., 52(8), 2298-2307. https://doi.org/10.1109/tsp.2004.831028
  40. Savino, P., Gherlone, M. and Tondolo, F. (2019), "Shape sensing with inverse finite element method for slender structures", Struct. Eng. Mech., Int. J., 72(2), 217-227. https://doi.org/10.12989/sem.2019.72.2.217
  41. Scaioni, M., Marsella, M., Crosetto, M., Tornatore, V. and Wang, J. (2018), "Geodetic and remote-sensing sensors for dam deformation monitoring", Sensors, 18(11), 3682. https://doi.org/10.3390/s18113682
  42. Seo, H. (2021a), "Tilt mapping for zigzag-shaped concrete panel in retaining structure using terrestrial laser scanning", J. Civil Struct. Health Monitor. https://doi.org/10.1007/s13349-021-00484-x
  43. Seo, H. (2021b), "Long-term Monitoring of zigzag-shaped concrete panel in retaining structure using laser scanning and analysis of influencing factors", Optics Lasers Eng., 139, 106498. https://doi.org/10.1016/j.optlaseng.2020.106498.
  44. Song, X. and Liang, D. (2018), "Dynamic displaceMent prediction of beam structures using fiber bragg grating sensors", Optik, 158, 1410-1416. https://doi.org/10.1016/j.ijleo.2018.01.013
  45. 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. https://doi.org/10.12989/sss.2013.11.4.365
  46. Sun, L.M., Zhang, W. and Nagarajaiah, S. (2019), "Bridge real-time damage identification method using inclination and strain measurements in the presence of temperature variation", J. Bridge Eng., 24(2), 04018111. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001325
  47. Tessler, A. and Spangler, J.L. (2004), "Inverse FEM for full-field reconstruction of elastic deformations in shear deformable plates and shells", Proceedings of the 2nd European Workshop on Structural Health Monitoring, Munich, Germany, January.
  48. Thomas, J., Gurusamy, S., Rajanna, T.R. and Asokan, S. (2020), "Structural shape estimation by mode shapes using fiber Bragg grating sensors: A genetic algorithm approach", IEEE Sensors J., 20(6), 2945-2952. https://doi.org/10.1109/Jsen.2019.2934366
  49. Tong, K.H., Bakhary, N., Kueh, A.B.H. and Yassin, A.Y.M. (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
  50. Tuia, D., Verrelst, J., Alonso, L., Perez-Cruz, F. and Camps-Valls, G. (2011), "Multioutput Support Vector Regression for Remote Sensing Biophysical Parameter Estimation", IEEE Geosci. Remote Sens. Lett., 8(4), 804-808. https://doi.org/10.1109/lgrs.2011.2109934
  51. Xiang, H. (2013), Higher Bridge Design Theory, China Communications Press, Beijing, China. [In Chinese]
  52. Xu, J., Zhang, H., Zhu, X., Li, L. and Ding, P. (2013), "Curve surface fitting based on an improved genetic algorithm", Proceedings of 2013 6th International Congress on Image and Signal Processing, Hangzhou, China, December.
  53. 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
  54. Zona, A. (2021), "Vision-based vibration monitoring of structures and infrastructures: An overview of recent applications", Infrastruct., 6(1), 4. https://doi.org/10.3390/infrastructures6010004