Smart Structures and Systems

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Optimal sensor placement under uncertainties using a nondirective movement glowworm swarm optimization algorithm

  • Zhou, Guang-Dong (College of Civil and Transportation Engineering, Hohai University) ;
  • Yi, Ting-Hua (School of Civil Engineering, Dalian University of Technology) ;
  • Zhang, Huan (School of Civil Engineering, Dalian University of Technology) ;
  • Li, Hong-Nan (College of Civil and Transportation Engineering, Hohai University)
  • Received : 2014.02.12
  • Accepted : 2014.05.02
  • Published : 2015.08.25
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Abstract

Optimal sensor placement (OSP) is a critical issue in construction and implementation of a sophisticated structural health monitoring (SHM) system. The uncertainties in the identified structural parameters based on the measured data may dramatically reduce the reliability of the condition evaluation results. In this paper, the information entropy, which provides an uncertainty metric for the identified structural parameters, is adopted as the performance measure for a sensor configuration, and the OSP problem is formulated as the multi-objective optimization problem of extracting the Pareto optimal sensor configurations that simultaneously minimize the appropriately defined information entropy indices. The nondirective movement glowworm swarm optimization (NMGSO) algorithm (based on the basic glowworm swarm optimization (GSO) algorithm) is proposed for identifying the effective Pareto optimal sensor configurations. The one-dimensional binary coding system is introduced to code the glowworms instead of the real vector coding method. The Hamming distance is employed to describe the divergence of different glowworms. The luciferin level of the glowworm is defined as a function of the rank value (RV) and the crowding distance (CD), which are deduced by non-dominated sorting. In addition, nondirective movement is developed to relocate the glowworms. A numerical simulation of a long-span suspension bridge is performed to demonstrate the effectiveness of the NMGSO algorithm. The results indicate that the NMGSO algorithm is capable of capturing the Pareto optimal sensor configurations with high accuracy and efficiency.

Keywords

Acknowledgement

Supported by : National Natural Science Foundation of China, Science Foundation of Jiangsu Province of China

References

  1. Arangio, S. and Beck, J.L. (2012), "Bayesian neural networks for bridge integrity assessment", Struct. Control. Health., 19(1), 3-21. https://doi.org/10.1002/stc.420
  2. Azarbayejani, M., El-Osery, A.I., Choi, K.K. and Taha, M.R. (2008), "A probabilistic approach for optimal sensor allocation in structural health monitoring", Smart. Mater. Struct., 17(5), 055019. https://doi.org/10.1088/0964-1726/17/5/055019
  3. Beck, J.L. and Katafygiotis, L.S. (1998), "Updating models and their uncertainties I: Bayesian statistical framework", J. Eng. Mech. -ASCE, 124(4), 455-461. https://doi.org/10.1061/(ASCE)0733-9399(1998)124:4(455)
  4. Bharat, T.V. (2008), "Agents based algorithms for design parameter estimation in contaminant transport inverse problems", IEEE Swarm Intelligence Symposium, USA, 1-7.
  5. Carne, T.G. and Dohmann, C.R. (1995), "A modal test design strategy for modal correlation", Proceedings of the 13th International Modal Analysis Conference, USA, 927-933.
  6. Chow, H.M., Lam, H.F., Yin, T. and Au, S.K. (2011), "Optimal sensor configuration of a typical transmission tower for the purpose of structural model updating", Struct. Control. Health., 18(3), 305-320. https://doi.org/10.1002/stc.372
  7. Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T.A.M.T. (2002), "A fast and elitist multiobjective genetic algorithm: NSGA-II", IEEE. T. Evolut. Comput., 6(2), 182-197. https://doi.org/10.1109/4235.996017
  8. 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. Pr., 24(4), 891-903. https://doi.org/10.1016/j.ymssp.2009.09.003
  9. Fonseca, C.M. and Fleming, P.J. (1995), "An overview of evolutionary algorithms in multiobjective optimization", Evolutionary Comput., 3(1), 1-16. https://doi.org/10.1162/evco.1995.3.1.1
  10. Friswell, M. and Mottershead, J. E. (1995), Finite Element Model Updating in Structural Dynamics, Kluwer Academic Publishers.
  11. Gong, Q., Zhou, Y. and Luo, Q. (2011), "Hybrid artificial glowworm swarm optimization algorithm for solving multi-dimensional knapsack problem", Procedia Engineering, 15, 2880-2884. https://doi.org/10.1016/j.proeng.2011.08.542
  12. Hamming, R.W. (1950), "Error detecting and error correcting codes", Bell. System. Technical. J., 29(2), 147-160. https://doi.org/10.1002/j.1538-7305.1950.tb00463.x
  13. Heredia-Zavoni, E. and Esteva, E.L. (1998), "Optimal instrumentation of uncertain structural systems subject to earthquake ground motions", 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
  14. Huang, K., Zhou, Y. and Wang, Y. (2011), "Niching glowworm swarm optimization algorithm with mating behavior", J. Inform.Comput. Sci,, 8, 4175-4184.
  15. Jaynes, E.T. (1978), "Where do we stand on maximum entropy", The maximum entropy formalism, 15, 118.
  16. Kammer, D.C. (1991), "Sensor placement for on-orbit modal identification and correlation of large space structures", J. Guid. Control. Dynam., 14(2), 251-259. https://doi.org/10.2514/3.20635
  17. Kang, F., Li, J.J. and Xu, Q. (2008), "Virus coevolution partheno-genetic algorithms for optimal sensor placement", Adv. Eng. Inform., 22(3), 362-370. https://doi.org/10.1016/j.aei.2008.02.001
  18. Kang, F., Li, J. and Li, H. (2013), "Artificial bee colony algorithm and pattern search hybridized for global optimization", Appl. Soft. Comput., 13(4), 1781-1791. https://doi.org/10.1016/j.asoc.2012.12.025
  19. Konak, A., Coit, D.W. and Smith, A.E. (2006), "Multi-objective optimization using genetic algorithms: A tutorial", Reliab. Eng. Syst. Safe, 91(9), 992-1007. https://doi.org/10.1016/j.ress.2005.11.018
  20. Krishnanand, K.N. and Ghose, D. (2005), "Detection of multiple source locations using a glowworm metaphor with applications to collective robotics", IEEE swarm intelligence symposium, California, USA, 84-91.
  21. Krishnanand, K.N. and Ghose, D. (2009), "Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions", Swarm Intelligence, 3(2), 87-124. https://doi.org/10.1007/s11721-008-0021-5
  22. Liao, W.H., Kao, Y. and Li, Y.S. (2011), "A sensor deployment approach using glowworm swarm optimization algorithm in wireless sensor networks", Expert Systems with Applications, 38(10), 12180-12188. https://doi.org/10.1016/j.eswa.2011.03.053
  23. Li, J. and Law, S.S. (2012a), "Damage identification of a target substructure with moving load excitation", Mech. Syst. Signal. Pr., 30, 78-90. https://doi.org/10.1016/j.ymssp.2012.02.002
  24. Li, J., Law, S.S. and Ding, Y. (2012b), "Substructure damage identification based on response reconstruction in frequency domain and model updating", Eng. Struct., 41, 270-284. https://doi.org/10.1016/j.engstruct.2012.03.035
  25. Mufti, A.A. (2002), "Structural health monitoring of innovative Canadian civil engineering structures", Struct. Health. Monit., 1(1), 89-103. https://doi.org/10.1177/147592170200100106
  26. Ngatchou, P.N., Fox, W.L. and El.Sharkawi, M.A. (2005), "Distributed sensor placement with sequential particle swarm optimization", Swarm Intelligence Symposium, Proceedings 2005 IEEE, 385-388.
  27. Nie, H., Shen, J. and Li, X. (2014), "Research on glowworm swarm optimization with ethnic division", J. Networks, 9(2), 458-465.
  28. Ntotsios, E., Christodoulou, K. and Papadimitriou, C. (2006), "Optimal sensor location methodology for structural identification and damage detection", Proceedings of the 3rd European Workshop on Structural Health Monitoring, Granada, Spain.
  29. 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
  30. Papadimitriou, C. (2004), "Optimal sensor placement methodology for parametric identification of structural systems", J. Sound. Vib., 278(4), 923-947. https://doi.org/10.1016/j.jsv.2003.10.063
  31. Papadimitriou, C. (2005), "Pareto optimal sensor locations for structural identification", Comput. Method. Appl. M., 194(12-16), 1655-1673. https://doi.org/10.1016/j.cma.2004.06.043
  32. Shi, Z.Y., Law, S.S. and Zhang, L.M. (2000), "Optimum sensor placement for structural damage detection", J. Eng. Mech. -ASCE, 126(11), 1173-1179. https://doi.org/10.1061/(ASCE)0733-9399(2000)126:11(1173)
  33. Spencer, B.F., Ruiz-Sandoval, M.E. and Kurata, N. (2004), "Smart sensing technology: opportunities and challenges", Struct. Control. Hlth., 11(4), 349-368. https://doi.org/10.1002/stc.48
  34. Udwadia, F.E. (1994), "Methodology for optimal sensor locations for parameter identification in dynamic systems", J. Eng. Mech.-ASCE, 120(3), 68-90.
  35. Yang, Y., Zhou, Y. and Gong, Q. (2010), "Hybrid artificial glowworm swarm optimization algorithm for solving system of nonlinear equations", J. Comput. Inform. Syst., 6(10), 3431-3438.
  36. Yao, L., Sethares, W.A. and Kammer, D C. (1993), "Sensor placement for on orbit modal identification via a genetic algorithm". AIAA J., 31(10), 1922-1928. https://doi.org/10.2514/3.11868
  37. Ye, S.Q. and Ni, Y.Q. (2012), "Information entropy based algorithm of sensor placement optimization for structural damage detection", Smart. Struct. Syst., 10(4-5), 443-458. https://doi.org/10.12989/sss.2012.10.4_5.443
  38. Yi, T.H., and Li, H.N. (2012), "Methodology developments in sensor placement for health monitoring of civil infrastructures", Int. J. Distrib. Sens. N., Article ID 612726
  39. Yi, T.H., Li, H.N. and Gu, M. (2011a), "Optimal sensor placement for health monitoring of high-rise structure based on genetic algorithm", Math. Probl. Eng., Article ID 395101.
  40. Yi, T.H., Li, H.N. and Gu, M. (2011b), "Optimal sensor placement for structural health monitoring based on multiple optimization strategies. Struct. Des. Tall. Spec., 20(7), 881-900. https://doi.org/10.1002/tal.712
  41. Yi, T. H., Li, H. N. and Gu, M. (2013), "Recent research and applications of GPS-based monitoring technology for high-rise structures", Struct. Control. Health., 20(5), 649-670. https://doi.org/10.1002/stc.1501
  42. Yi, T.H., Li, H.N. and Zhang, X.D. (2012a). "A modified monkey algorithm for optimal sensor placement in structural health monitoring", Smart. Mater. Struct., 21(10), 105033. https://doi.org/10.1088/0964-1726/21/10/105033
  43. Yi, T.H., Li, H.N. and Zhang, X.D. (2012b), "Sensor placement on Canton Tower for health monitoring using asynchronous-climb monkey algorithm", Smart. Mater. Struct., 21(12), 125023. https://doi.org/10.1088/0964-1726/21/12/125023
  44. Yuen, K.V., Katafygiotis, L.S., Papadimitriou, C. and Mickleborough, N.C. (2001), "Optimal sensor placement methodology for identification with unmeasured excitation," J. Dyn. Syst-T. Asme., 123(4), 677-686. https://doi.org/10.1115/1.1410929
  45. Zainal, N., Zain, A.M., Radzi, N.H.M. and Udin, A. (2013), "Glowworm swarm optimization (GSO) algorithm for optimization problems: A state-of-the-art review", Appl. Mech. Mater., 421, 507-511. https://doi.org/10.4028/www.scientific.net/AMM.421.507
  46. Zhou, G.D. and Yi, T.H. (2013a), "Recent developments on wireless sensor networks technology for bridge health monitoring", Math. Probl. Eng., Article ID 947867.
  47. Zhou, G.D. and Yi, T.H. (2013b), "Thermal load in large-scale bridges: a state-of-the-art review", Int. J. Distrib. Sens. N., Article ID 797650.
  48. Zhou, G.D. and Yi, T.H. (2013c), "The nonuniform node configuration of wireless sensor networks for long-span bridge health monitoring", Int. J. Distrib. Sens. N., Article ID 797650.
  49. Zhou, G.D. and Yi, T.H. (2013d), "The node arrangement methodology of wireless sensor networks for long-span bridge health monitoring", Int. J. Distrib. Sens. N., Article ID 865324.
  50. Zhou, Y., Zhou, G., Wang, Y. and Zhao, G. (2013), "A glowworm swarm optimization algorithm based tribes", Appl. Math. Inform. Sci., 7(2), 537-541. https://doi.org/10.12785/amis/072L24

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