Structural Engineering and Mechanics

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

A MOM-based algorithm for moving force identification: Part II - Experiment and comparative studies

  • Yu, Ling (Key Lab of Disaster Forecast and Control in Engineering, Ministry of Education of the People's Republic of China (Jinan University), Department of Civil and Structural Engineering, The Hong Kong Polytechnic University) ;
  • Chan, Tommy H.T. (School of Urban Development, Faculty of Built Environment & Engineering, Queensland University of Technology, Department of Civil and Structural Engineering, The Hong Kong Polytechnic University) ;
  • Zhu, Jun-Hua (Key Lab of Disaster Forecast and Control in Engineering, Ministry of Education of the People's Republic of China (Jinan University), Changjiang River Scientific Research Institute)
  • Received : 2006.08.30
  • Accepted : 2007.08.07
  • Published : 2008.05.30
⟨ Previous Next ⟩

Abstract

A MOM-based algorithm (MOMA) has been developed for moving force identification from dynamic responses of bridge in the companion paper. This paper further evaluates and investigates the properties of the developed MOMA by experiment in laboratory. A simply supported bridge model and a few vehicle models were designed and constructed in laboratory. A series of experiments have then been conducted for moving force identification. The bending moment and acceleration responses at several measurement stations of the bridge model are simultaneously measured when the model vehicle moves across the bridge deck at different speeds. In order to compare with the existing time domain method (TDM), the best method for moving force identification to date, a carefully comparative study scheme was planned and conducted, which includes considering the effect of a few main parameters, such as basis function terms, mode number involved in the identification calculation, measurement stations, executive CPU time, Nyquist fraction of digital filter, and two different solutions to the ill-posed system equation of moving force identification. It was observed that the MOMA has many good properties same as the TDM, but its CPU execution time is just less than one tenth of the TDM, which indicates an achievement in which the MOMA can be used directly for real-time analysis of moving force identification in field.

Keywords

References

  1. Bilello, C., Bergman, L.A. and Kuchma, D. (2004), "Experimental investigation of a small-scale bridge model under a moving mass", J. Struct. Eng., 130(5), 799-804 https://doi.org/10.1061/(ASCE)0733-9445(2004)130:5(799)
  2. Busby, H.R. and Trujillo, D.M. (1997), "Optimal regularization of an inverse dynamic problem", Comput. Struct., 63(2), 243-248 https://doi.org/10.1016/S0045-7949(96)00340-9
  3. Cantineni, R. (1992), "Dynamic behavior of highway bridges under the passage of heavy vehicles", Swiss Federal Laboratories for Materials Testing and Research (EMPA) Report No. 220, 240
  4. Cebon, D. (1987), "Assessment of the dynamic wheel forces generated by heavy road vehicles", Symposium on Heavy Vehicle Suspension and Characteristics, Austrian Road Research Board
  5. Chan, T.H.T. and O'Conner, C. (1990), "Wheel loads from highway bridge strains: Field studies", J. Struct. Eng., ASCE, 116(7), 1751-1771 https://doi.org/10.1061/(ASCE)0733-9445(1990)116:7(1751)
  6. Chan, T.H.T., Law, S.S. and Yung, T.H. (2000), "Moving force identification using an existing prestressed concrete bridge", Eng. Struct., 22, 1261-1270 https://doi.org/10.1016/S0141-0296(99)00084-X
  7. Chan, T.H.T., Yu, L. and Law, S.S. (2000), "Comparative studies on moving force identification from bridge strains in laboratory", J. Sound Vib., 235(1), 87-104 https://doi.org/10.1006/jsvi.2000.2909
  8. Hansen, P.C. (1992), "Analysis of discrete ill-posed problems by means of the L curve", SIAM Rev., 34, 561-580 https://doi.org/10.1137/1034115
  9. Hansen, P.C. and O'Leavy, D.P. (1993), "The use of the L curve in the regularization of discrete ill-posed problems", SIAM J. Sci. Comput. (USA), 14, 1487-1503 https://doi.org/10.1137/0914086
  10. Harris, H.G. and Sabnis, G.M. (2000), Structural Modeling and Experimental Techniques, CRC Press, New York
  11. Law, S.S., Chan, T.H.T., Zhu, X.Q. and Zeng, Q.H. (2001), "Regularization in moving force identification", J. Eng. Mech., ASCE, 127(2), 136-148 https://doi.org/10.1061/(ASCE)0733-9399(2001)127:2(136)
  12. Law, S.S., Chanm T.H.T. and Zeng, Q.H. (1997), "Moving force identification: A time domain method", J. Sound Vib., 201(1), 1-22 https://doi.org/10.1006/jsvi.1996.0774
  13. Olsson, M. (1991), "On the fundamental moving load problem", J. Sound Vib., 145(2), 299-307 https://doi.org/10.1016/0022-460X(91)90593-9
  14. Proakis, G.J. and Manolakis, D.G. (1996), Digital Signal Processing, Principle, Algorithms and Applications, Prentice-Hall Inc
  15. Santantamarina, J.C. and Fratta, D. (1998), "Introduction to discrete signals and inverse problems in civil engineering", ASCE, Reston. Va., 200-238
  16. Tikhonov, A.N. and Arsenin, V.Y. (1977), Solution of Ill-posed Problems. Wiley. New York
  17. Yu, L. (2002), "Accounting for bridge dynamic loads using moving force identification system (MFIS)", Ph.D. Thesis, The Hong Kong Polytechnic University, Hong Kong
  18. Yu, L. and Chan, T.H.T. (2002), "Moving force identification from bending moment responses of bridge", Struct. Eng. Mech., 14(2), 151-170 https://doi.org/10.12989/sem.2002.14.2.151
  19. Yu, L. and Chan, T.H.T. (2003), "Moving force identification based on the frequency-time domain method", J. Sound Vib., 261, 329-349 https://doi.org/10.1016/S0022-460X(02)00991-4
  20. Yu, L. and Chan, T.H.T. (2005), "Moving force identification from bridge dynamic responses", Struct. Eng. Mech., 21(3), 369-374 https://doi.org/10.12989/sem.2005.21.3.369
  21. Yu, L., Chan, T.H.T. and Zhu, J.H. (2008), "A MOM-based algorithm for moving force identification: Part I-theory and numerical simulation", Struct. Eng. Mech., 29(2), 135-154 https://doi.org/10.12989/sem.2008.29.2.135

Cited by

  1. Moving train loads and parameters identification on a steel truss girder model vol.15, pp.1, 2015, https://doi.org/10.1007/s13296-015-3012-6
  2. Moving force identification based on redundant concatenated dictionary and weighted l 1 -norm regularization vol.98, 2018, https://doi.org/10.1016/j.ymssp.2017.04.032
  3. Numerical study on moving train parameter identification system through a simply supported bridge vol.26, pp.9, 2012, https://doi.org/10.1007/s12206-012-0720-0
  4. Recent developments in inverse problems of vehicle–bridge interaction dynamics vol.6, pp.1, 2016, https://doi.org/10.1007/s13349-016-0155-x
  5. Forced vibration of an embedded single-walled carbon nanotube traversed by a moving load using nonlocal Timoshenko beam theory vol.11, pp.1, 2011, https://doi.org/10.12989/scs.2011.11.1.059
  6. Nonlinear vibrations of a multi-span continuous beam subject to periodic impact excitation vol.50, pp.5, 2015, https://doi.org/10.1007/s11012-014-0092-x
  7. Beam structural system moving forces active vibration control using a combined innovative control approach vol.12, pp.2, 2013, https://doi.org/10.12989/sss.2013.12.2.121
  8. Experimental Study on Moving Train Loads Identification from Bridge Responses vol.143-144, pp.1662-8985, 2010, https://doi.org/10.4028/www.scientific.net/AMR.143-144.32
  9. Multi-Axle Moving Train Loads Identification by Using Fuzzy Pattern Recognition Technique vol.29-32, pp.1662-7482, 2010, https://doi.org/10.4028/www.scientific.net/AMM.29-32.1307
  10. Multi-Axle Moving Train Loads Identification on Continuous Bridge from Bridge Displacement Responses vol.239-240, pp.1662-7482, 2012, https://doi.org/10.4028/www.scientific.net/AMM.239-240.670
  11. A sparse self-estimated sensor-network for reconstructing moving vehicle forces vol.28, pp.8, 2008, https://doi.org/10.1088/1361-665x/ab28ed
  12. Toward efficacy of piecewise polynomial truncated singular value decomposition algorithm in moving force identification vol.22, pp.12, 2019, https://doi.org/10.1177/1369433219849817