Smart Structures and Systems

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Entropy-based optimal sensor networks for structural health monitoring of a cable-stayed bridge

  • Azarbayejani, M. (Dept. of Civil Engineering, University of New Mexico) ;
  • El-Osery, A.I. (Dept. of Electrical Engineering, New Mexico Tech) ;
  • Taha, M.M. Reda (Dept. of Civil Engineering, University of New Mexico)
  • Received : 2008.08.20
  • Accepted : 2008.09.27
  • Published : 2009.07.25
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Abstract

The sudden collapse of Interstate 35 Bridge in Minneapolis gave a wake-up call to US municipalities to re-evaluate aging bridges. In this situation, structural health monitoring (SHM) technology can provide the essential help needed for monitoring and maintaining the nation's infrastructure. Monitoring long span bridges such as cable-stayed bridges effectively requires the use of a large number of sensors. In this article, we introduce a probabilistic approach to identify optimal locations of sensors to enhance damage detection. Probability distribution functions are established using an artificial neural network trained using a priori knowledge of damage locations. The optimal number of sensors is identified using multi-objective optimization that simultaneously considers information entropy and sensor cost-objective functions. Luling Bridge, a cable-stayed bridge over the Mississippi River, is selected as a case study to demonstrate the efficiency of the proposed approach.

Keywords

References

  1. AASHTO (2006), LRFD Bridge Design Specifications, Manual for Condition Evaluation of Bridges, 2nd Edition, American Association of State Highway and Transportation Officials, Washington, D.C.
  2. Achenbach, J.D. (2007), "On the road from schedule-based nondestructive inspection to structural health monitoring", Proc. of the 6th Int. Workshop on Structural Health Monitoring, Stanford University, Stanford, CA, DEStech Publications, 16-28.
  3. Adams, D.E. (2007), Health Monitoring of Structural Materials and Components, John Wiley & Sons, West Sussex, UK.
  4. Beard, S., Liu, B., Qing, P. and Zhang, D. (2007), "Challenges in implementation of SHM", Proc. of the 6th Int. Workshop on Structural Health Monitoring, Stanford University, Stanford, CA. DEStech Publications, 65-81.
  5. Chang, F.K., Markmiller, J.F.C., Ihn, J.B. and Cheng, K.Y. (2007), "A potential link from damage diagnostics to health prognostics of composites through built-in sensors", J. Vib. Acoust., 129(6), 718-729. https://doi.org/10.1115/1.2730530
  6. Doebling, S.W., Farrar, C.R., Prime, M.B. and Shevitz, D.W. (1996), "Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: a literature review", Los Alamos National Laboratory report, LA-13070-MS.
  7. Fritzen, C.P. and Bohle, K. (2001), "Application of model-based damage identification to a seismically loaded structure", Smart Mater. Struct., 10, 452-458. https://doi.org/10.1088/0964-1726/10/3/305
  8. Guratzsch, R.F. and Mahadevan, S. (2006), "Sensor placement design for SHM under uncertainty", Proc. of the Third European Workshop: Structural Health Monitoring, Granada, Spain, 1168-1175.
  9. Heredia-Zavoni, E. and Esteva, E.L. (1998), "Optimal instrumentation of uncertain structural systems subject to earthquake motion", Earthq. Eng. Struct. D., 27(4), 343-362. https://doi.org/10.1002/(SICI)1096-9845(199804)27:4<343::AID-EQE726>3.0.CO;2-F
  10. Ho, Y.K. and Ewins, D.J. (2000), "On the structural damage identification with mode shapes", European COST F3 Conf. on System Identification and Structural Health Monitoring, Madrid, Spain, 677-686.
  11. Kumar, K.R. and Narayanan, S. (2008), "Active vibration control of beams with optimal placement of piezoelectric sensor/actuator pairs", Smart Mater. Struct., 17(2008) 055008. https://doi.org/10.1088/0964-1726/17/5/055008
  12. Kumara, S., Suh, J. and Mysore, S.P. (1999), "Machinery fault diagnosis and prognosis: application of advanced signal processing techniques", CIRP Ann-Manuf. Techn., 48(1), 317-320. https://doi.org/10.1016/S0007-8506(07)63192-8
  13. Maeck, J. and De Roeck, G. (1999), "Damage detection on a prestressed concrete bridge and RC beams using dynamic system identification", Damage Assessment of Structures, Proc. Of the Int. Conf. on Damage Assessment of Structures (DAMAS 99), Dublin, Ireland, 320-327.
  14. Miettinen, K. (1999), Nonlinear Multi-objective Optimization, Kluwer, Boston, MA.
  15. Natke, H.G. and Cempel, C. (1997), "Model-Aided Diagnosis Based on Symptoms", Structural Damage Assessment Using Advanced Signal Processing Procedures, Proc. of DAMAS 97, UK, 363-375.
  16. Neild, S.A., McFadden, P.D. and Williams, M.S. (2003), "A review of time-frequency methods for structural vibration analysis", Eng. Struct., 25, 713-728. https://doi.org/10.1016/S0141-0296(02)00194-3
  17. Ntotsios, E., Christodoulou, K. and Papadimitriou, C. (2006). "Optimal sensor location methodology for structural identification and damage detection", Proc. of the Third European Workshop: Structural Health Monitoring. Granada, Spain, 1160-1167.
  18. Osyczka, A. (1984), Multi Criterion Optimization in Engineering, Ellis Horwood Series, London, UK.
  19. 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
  20. Pareto, V. (1971), Manual of Political Economy, Augustus M. Kelley, NY.
  21. Parker, D.L., Frazier, W.G., Rinehart, H.S. and Cuevas, P.S. (2006), "Experimental validation of optimal sensor placement algorithms for structural health monitoring", Proc. of the Third European Workshop: Structural Health Monitoring 2006, DEStech Publications, 1144-1150.
  22. Pothisiri, T. and Hjelmstad, K.D. (2003), "Structural damage detection and assessment from modal response", J. Eng. Mech. ASCE, 129(2), 135-145. https://doi.org/10.1061/(ASCE)0733-9399(2003)129:2(135)
  23. Pulthasthan, S. and Pota, H.R. (2008), "The optimal placement of actuator and sensor for active noise control of sound-structure interaction systems", Smart Mater. Struct., 17 (2008) 037001. https://doi.org/10.1088/0964-1726/17/3/037001
  24. Rao, A.R.M. and Anandakumar, G. (2007), "Optimal placement of sensors for structural system identification and health monitoring using a hybrid swarm intelligence technique", Smart Mater. Struct., 16, 2658-2672. https://doi.org/10.1088/0964-1726/16/6/071
  25. Reda Taha, M., Noureldin, A., Osman, A. and El-Sheimy, N. (2004), "Introduction to the use of wavelet multiresolution analysis for intelligent structural health monitoring", Can. J. Civil Eng., 31(5), 719-731. https://doi.org/10.1139/l04-022
  26. Ross, T.J. (2004), Fuzzy Logic with Engineering Applications, John Wiley & Sons, Chichester, UK.
  27. Saltelli, A., Chan, K. and E.M. Scott, Eds. (2000), Sensitivity Analysis, John Wiley & Sons, West Sussex, UK.
  28. Schulte, R.T., Bohle, K., Fritzen, C.P. and Schuhmacher, G. (2006), "Optimal sensor placement for damage identification - An efficient forward-backward selection algorithm", Proc. of the Third European Workshop: Structural Health Monitoring, DEStech Publications, 1151-1159.
  29. Shannon, C.E. (1948), "A mathematical theory of communication", Bell System Technical Journal, 27, 379-423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
  30. Stanbridge, A.B., Khan, A.Z. and Ewins, D.J. (1997), "Fault identification in vibrating structures using a scanning laser doppler vibrometer", Structural Health Monitoring, Current Status and Perspectives, Stanford University, Palo Alto, California, 56-65.
  31. Thompson, R.B. (1999), "Overview of the ETC POD Methodology", Review of Progress in QNDE, 18, 2295-2304.
  32. Udwadia, F.E. (1994), "Methodology for optimal sensor locations for parameter identification in dynamic systems", J. Eng. Mech. ASCE, 120(2), 368-390. https://doi.org/10.1061/(ASCE)0733-9399(1994)120:2(368)
  33. USA TODAY (Friday, July 25, 2008), Billions needed to shore up bridges, pp. 1 and 10A.

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