Smart Structures and Systems

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Stochastic DLV method for steel truss structures: simulation and experiment

  • An, Yonghui (Department of Civil Engineering, Dalian University of technology) ;
  • Ou, Jinping (Department of Civil Engineering, Dalian University of technology) ;
  • Li, Jian (Department of Civil, Environmental, and Architectural Engineering, University of Kansas) ;
  • Spencer, B.F. Jr. (Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign)
  • Received : 2012.02.01
  • Accepted : 2013.06.28
  • Published : 2014.08.25
⟨ Previous Next ⟩

Abstract

The stochastic damage locating vector (SDLV) method has been studied extensively in recent years because of its potential to determine the location of damage in structures without the need for measuring the input excitation. The SDLV method has been shown to be a particularly useful tool for damage localization in steel truss bridges through numerical simulation and experimental validation. However, several issues still need clarification. For example, two methods have been suggested for determining the observation matrix C identified for the structural system; yet little guidance has been provided regarding the conditions under which the respective formulations should be used. Additionally, the specific layout of the sensors to achieve effective performance with the SDLV method and the associated relationship to the specific type of truss structure have yet to be explored. Moreover, how the location of truss members influences the damage localization results should be studied. In this paper, these three issues are first investigated through numerical simulation and subsequently the main results are validated experimentally. The results of this paper provide guidance on the effective use of the SDLV method.

Keywords

References

  1. An, Y.H. and Ou, J.P. (2010), "Research on a long-span truss steel bridge Benchmark model for health monitoring", Proceedings of the 19th national Bridge Conference (China), Shanghai, June, 1284-1291 (in Chinese).
  2. An, Y.H. and Ou, J.P. (2012), "Experimental and numerical studies on damage localization of simply supported beams based on curvature difference probability method of waveform fractal dimension", J. Intel. Mat. Syst. Str., 23, 415-426. https://doi.org/10.1177/1045389X11434172
  3. An, Y.H. and Ou, J.P. (2013), "Experimental and numerical studies on model updating method of damage severity identification utilizing four cost functions", J. Struct. Control Health Monit., 20(1), 107-120. https://doi.org/10.1002/stc.480
  4. Bernal, D. (2006), "Flexibility-based damage localization from stochastic realization results", J. Eng. Mech. - ASCE, 132(6), 651-658. https://doi.org/10.1061/(ASCE)0733-9399(2006)132:6(651)
  5. Bernal, D. (2002), "Load vectors for damage localization", J. Eng. Mech. - ASCE, 128(1), 7-14. https://doi.org/10.1061/(ASCE)0733-9399(2002)128:1(7)
  6. Balmes, E., Basseville, M., Mevel, L., Nasser, H. and Zhou, W. (2008), "Statistical model-based damage localization: A combined subspace-based and substructuring approach", Struct. Control Health Monit., 15(6), 857-875. https://doi.org/10.1002/stc.223
  7. 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. - ASCE, 130(1), 49-60. https://doi.org/10.1061/(ASCE)0733-9399(2004)130:1(49)
  8. Chen, B.L. and Nagarajaiah, S. (2008), "Structural damage detection using decentralized controller design method," Smart Struct. Syst., 4(6), 779-794. https://doi.org/10.12989/sss.2008.4.6.779
  9. Deraemaeker, A., Preumont, A., Reynders, E., De Roeck, G., Kullaa, J., Lamsa, V., Worden, K., Manson, G., Barthorpe, R., Papatheou, E., Kudela, P., Malinowski, P., Ostachowicz , W. and Wandowski, T. (2010), "Vibration-based structural health monitoring using large sensor networks", Smart Struct. Syst., 6(3), 335-347. https://doi.org/10.12989/sss.2010.6.3.335
  10. Gao, Y. (2005), Strctural health monitoring strategies for smart sensor networks, PhD Thesis, Graduate College of the University of Illinois at Urbana-Champaign, U.S.A.
  11. Gao, Y., Spencer Jr, B.F. and Bernal, D. (2007), "Experimental verification of the flexibility-based damage locating vector method", J. Eng. Mech. - ASCE , 133(10), 1043-1049. https://doi.org/10.1061/(ASCE)0733-9399(2007)133:10(1043)
  12. Hu, N., Wang, X., Fukunaga, H., Yao, Z.H., Zhang, H.X. and Wu, Z.S. (2001), "Damage assessment of structures using modal test data", Int. J. Solids Struct., 38(18), 3111-3126. https://doi.org/10.1016/S0020-7683(00)00292-4
  13. James, G.H., Carne, T.G. and Lauffer, J.P. (1995), "The natural excitation technique (NExT) for modal parameter extraction from operating structures", J. Anal. Expert. Modal Anal., 10(4), 260-277.
  14. Juang, J.N. and Pappa, R.S. (1985), "An eigensystem realization algorithm for modal parameter identification and model reduction", J. Guid. Control Dynam., 8(5), 620-627. https://doi.org/10.2514/3.20031
  15. Katkhuda, H. and Haldar, A. (2008), "A novel health assessment technique with minimum information", Struct. Control Health Monit., 15(6), 821-838. https://doi.org/10.1002/stc.221
  16. Kim, H. and Melhem, H. (2004), "Damage detection of structures by wavelet analysis", Eng. Struct., 26, 7-14.
  17. Lifshitz, J.M. and Rotem, A. (1969), "Determination of reinforcement unbonding of composites by a vibration technique", J. Compos. Mater., 3(3), 412-423. https://doi.org/10.1177/002199836900300305
  18. Mathwork Inc. (2005), Matlab user manual, Lowell, MA, U.S.A.
  19. Nagayama, T., Spencer Jr, B.F. and Rice, J.A. (2009), "Autonomous decentralized structural health monitoring using smart sensors", Struct.Control Health Monit., 16(7-8), 842-859.
  20. Ou J.P. (2005), "Research and practice of smart sensor networks and health monitoring systems for civil infrastructures in mainland China", Bulletin of National Science Foundation of China, 19(1), 8-12 (in Chinese).
  21. Pandey, A.K., Biswas, M. and Samman, M.M. (1991), "Damage detection from changes in curvature mode shapes", J. Sound Vib., 145(2), 321-332. https://doi.org/10.1016/0022-460X(91)90595-B
  22. Pandey, A. and Biswas, M. (1996), "Damage detection in structures using changes in flexibility", J. Sound Vib., 169(1), 3-17.
  23. Shi, Z.Y., Law, S.S. and Zhang, L.M. (2000), "Structural damage detection from modal strain energy change", J. Eng. Mech.- ASCE, 126(12), 1216-1223. https://doi.org/10.1061/(ASCE)0733-9399(2000)126:12(1216)
  24. Yan, Y.J., Cheng, L., Wu, Z.Y. and Yam, L.H. (2007), "Development in vibration-based structural damage detection technique", Mech. Syst. Signal Pr., 21, 2198-2211. https://doi.org/10.1016/j.ymssp.2006.10.002
  25. Yu, L.Y. and Giurgiutiu, V. (2005), "Advanced signal processing for enhanced damage detection with piezoelectric wafer active sensors", Smart Struct. Syst., 1(2), 185-215. https://doi.org/10.12989/sss.2005.1.2.185
  26. Zhou, D., Ha, D.S. and Inman D.J. (2010), "Ultra low-power active wireless sensor for structural health monitoring", Smart Struct. Syst., 6(5-6), 675-687. https://doi.org/10.12989/sss.2010.6.5_6.675
  27. Zhou, L., Yuan, F.G. and Meng, W.J. (2007), "A pre-stack migration method for damage identification in composite structures", Smart Struct. Syst., 3(4), 439-454. https://doi.org/10.12989/sss.2007.3.4.439

Cited by

  1. A two-stage assessment method using damage locating vector method and differential evolution algorithm for damage identification of cross-ply laminated composite beams vol.20, pp.12, 2017, https://doi.org/10.1177/1369433217695620
  2. Experimental and numerical studies on a test method for damage diagnosis of stay cables vol.20, pp.2, 2017, https://doi.org/10.1177/1369433216659927
  3. Axial Strain Accelerations Approach for Damage Localization in Statically Determinate Truss Structures vol.32, pp.4, 2017, https://doi.org/10.1111/mice.12258
  4. Real-time fast damage detection of shear structures with random base excitation vol.74, 2015, https://doi.org/10.1016/j.measurement.2015.07.006
  5. Rank-revealing QR decomposition applied to damage localization in truss structures vol.24, pp.2, 2017, https://doi.org/10.1002/stc.1849
  6. Fast Warning Method for Rigid Hangers in a High-Speed Railway Arch Bridge Using Long-Term Monitoring Data vol.31, pp.6, 2017, https://doi.org/10.1061/(ASCE)CF.1943-5509.0001097
  7. Field monitoring of the train-induced hanger vibration in a high-speed railway steel arch bridge vol.17, pp.6, 2016, https://doi.org/10.12989/sss.2016.17.6.1107
  8. Damage Identification by the Data Expansion and Substructuring Methods vol.2018, pp.None, 2014, https://doi.org/10.1155/2018/1867562