DOI QR코드

DOI QR Code

Optimal sensor placement for cable force monitoring using spatial correlation analysis and bond energy algorithm

  • Li, Shunlong (Department of Bridge and Tunnel engineering, Harbin Institute of Technology) ;
  • Dong, Jialin (Zhejiang Scientific Research Institute of Transport) ;
  • Lu, Wei (Department of Civil and Environment Engineering, Harbin Institute of Technology (Shenzhen), HIT Campus of Xili University Town) ;
  • Li, Hui (Department of Bridge and Tunnel engineering, Harbin Institute of Technology) ;
  • Xu, Wencheng (CCCC Highway Consultants CO., Ltd. (HPDI)) ;
  • Jin, Yao (CCCC Highway Consultants CO., Ltd. (HPDI))
  • Received : 2017.04.21
  • Accepted : 2017.11.14
  • Published : 2017.12.25

Abstract

Cable force monitoring is an essential and critical part of the safety evaluation of cable-supported bridges. A reasonable cable force monitoring scheme, particularly, sensor placement related to accurate safety assessment and budget cost-saving becomes a major concern of bridge administrative authorities. This paper presents optimal sensor placement for cable force monitoring by selecting representative sensor positions, which consider the spatial correlativeness existing in the cable group. The limited sensors would be utilized for maximizing useful information from the monitored bridges. The maximum information coefficient (MIC), mutual information (MI) based kernel density estimation, as well as Pearson coefficients, were all employed to detect potential spatial correlation in the cable group. Compared with the Pearson coefficient and MIC, the mutual information is more suitable for identifying the association existing in cable group and thus, is selected to describe the spatial relevance in this study. Then, the bond energy algorithm, which collects clusters based on the relationship of surrounding elements, is used for the optimal placement of cable sensors. Several optimal placement strategies are discussed with different correlation thresholds for the cable group of Nanjing No.3 Yangtze River Bridge, verifying the effectiveness of the proposed method.

Keywords

Acknowledgement

Supported by : National Natural Science Foundation of China

References

  1. Allemang, R.J. and Brown, D.L. (1982),"A correlation coefficient for modal vector analysis", Proceedings of the 1st international modal analysis conference, SEM, Orlando, USA, January.
  2. Baruh, H. and Choe, K. (1987), "Sensor-failure detection method for flexible structures", J. Guid. Control Dynam., 10(5), 474-482. https://doi.org/10.2514/3.20242
  3. Carne, T.G. and Dohrmann, C. (1994), "A modal test design strate gy for model correlation", Proceedings of the SPIE - The International Society for Optical Engineering, Nashville, USA, December.
  4. Dewolf, J.T. and Zhao, J. (1999), "Sensitivity study for vibrational parameters used in damage detection", J. Struct. Eng., 125(4), 410-416. https://doi.org/10.1061/(ASCE)0733-9445(1999)125:4(410)
  5. Ding, Z.H., Lu, Z.R. and Liu, J.K. (2017), "Parameters identificati on of chaotic systems based on artificial bee colony algorithm c ombined with cuckoo search strategy", Sci. China Technol. Sci., 1-10.
  6. Doebling, S.W. (1995), "Measurement of structural flexibility matrices for experiments with incomplete reciprocity", Proceedings of the International Symposium on Physical Design, Japan, January.
  7. Gorfine, M., Heller, R. and Heller, Y. (2015), Comment on "Detecting Novel Associations in Large Data Sets", Eprint Arxiv.
  8. Guyan, R.J. (1965), "Reduction of stiffness and mass matrices", Aiaa J., 3(2), 380-380. https://doi.org/10.2514/3.2874
  9. Jemielniak, K. (1999), "Commercial tool condition monitoring systems", Int. J. Adv. Manufact. Tech., 15(10), 711-721. https://doi.org/10.1007/s001700050123
  10. Jung, B.K., Cho, J.R. and Jeong, W.B. (2016), "Reproduction of vibration patterns of elastic structures by block-wise modal expansion method (bmem)", Smart Struct. Syst., 18(4), 819-837. https://doi.org/10.12989/sss.2016.18.4.819
  11. Kammer, D.C. (1991), "Sensor placement for On-Orbit modal identification and correlation of large space Structures", Proceedings of the American Control Conference, Boston, USA, June.
  12. Law, S.S. and Li, J. (2010), "Updating the reliability of a concrete bridge structure based on condition assessment with uncertainties", Eng. Struct., 32(1), 286-296. https://doi.org/10.1016/j.engstruct.2009.09.015
  13. Li, D., Li, H. and Fritzen, C. (2007), "The connection between effective independence and modal kinetic energy methods for sensor placement", J. Sound Vib., 305(4-5), 945-955. https://doi.org/10.1016/j.jsv.2007.05.004
  14. Li, D., Zhou, Z. and Ou, J. (2011), "Development and sensing properties study of FRP-FBG smart stay cable for bridge health monitoring applications", Measurement, 44(4), 722-729. https://doi.org/10.1016/j.measurement.2011.01.005
  15. Li, H., Ou, J. and Zhou, Z. (2009), "Applications of optical fibre Bragg gratings sensing technology-based smart stay cables", Opt. Laser. Eng., 47(10), 1077-1084. https://doi.org/10.1016/j.optlaseng.2009.04.016
  16. Lim, K.B. (2012), "Method for optimal actuator and sensor placement for large flexible structures", J. Guid. Control Dynam., 15(1), 49-57. https://doi.org/10.2514/3.20800
  17. Liu, W., Gao, W.C., Sun, Y. and Xu, M.J. (2008), "Optimal sensor placement for spatial lattice structure based on genetic algorithms", J. Sound Vib., 317(1), 175-189. https://doi.org/10.1016/j.jsv.2008.03.026
  18. Lu, L., Wang, X., Liao, L., Wei, Y., Huang, C. and Liu, Y. (2015), "Application of model reduction technique and structural subsection technique on optimal sensor placement of truss structures", Smart Struct. Syst., 15(2), 355-373. https://doi.org/10.12989/sss.2015.15.2.355
  19. Lu, W., Wen, R., Teng, J., Li, X. and Li, C. (2016), "Data correlation analysis for optimal sensor placement using a bond energy algorithm", Measurement, 91, 509-518. https://doi.org/10.1016/j.measurement.2016.05.089
  20. Mccormick, W.T. Jr. and Others, A. (1969), "Identification of data structures and relationships by matrix reordering techniques", Algorithms, 141.
  21. Mehrad, V., Xue, D. and Gu, P. (2013), "Prediction of surface reconstruction uncertainties for freeform surface inspection", Measurement, 46(8), 2682-2694. https://doi.org/10.1016/j.measurement.2013.04.025
  22. Moon, Y.I., Rajagopalan, B. and Lall, U. (1995), "Estimation of mutual information using kernel density estimators", Physical Review E Statistical Physics Plasmas Fluids & Related Interdisciplinary Topics, 52(3), 2318-2321. https://doi.org/10.1103/PhysRevE.52.2318
  23. 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., 15(4), 1041-1062. https://doi.org/10.12989/sss.2015.15.4.1041
  24. Penny, J., Friswell, M. and Garvey, S. (1994), "Automatic choice of measurement locations for dynamic testing", AIAA J., 32(2), 407-414. https://doi.org/10.2514/3.11998
  25. Reshef, D.N., Reshef, Y.A., Finucane, H.K., Grossman, S.R., Mcvean, G., Turnbaugh, P.J., Lander, E.S., Mitzenmacher, M. and Sabeti, P.C. (2011), "Detecting novel associations in large datasets", Science, 334(6062), 1518. https://doi.org/10.1126/science.1205438
  26. Shannon, C.E. (1951), "A mathematical theory of communication", The Quarterly Review of Biology, 26(3), 3-55.
  27. Simon, N. and Tibshirani, R. (2014), "Comment on "Detecting Novel Associations In Large Data Sets" by Reshef et al., Science Dec 16, 2011", Statistics.
  28. Speed, T. (2011), "A correlation for the 21st century", Science, 33 4(6062), 1502-1503.
  29. Steuer, R., Kurths, J., Daub, C.O., Weise, J. and Selbig, J. (2002), "The mutual information: detecting and evaluating dependencies between variables", Bioinformatics, 18 Suppl 2(suppl_2), S231-240. https://doi.org/10.1093/bioinformatics/18.suppl_2.S231
  30. Tong, K.H., Yassin, A.Y.M., Bakhary, N. and Kueh, A.B.H. (2014), "Optimal sensor placement for mode shapes using improved simulated annealing", Smart Struct. Syst., 13(3), 389-406. https://doi.org/10.12989/sss.2014.13.3.389
  31. Tourassi, G.D., Frederick, E.D., Markey, M.K. and Floyd Jr., C.E. (2001), "Application of the mutual information criterion for feature selection in computer-aided diagnosis", Medical Phys., 28(12), 2394. https://doi.org/10.1118/1.1418724
  32. Udwadia, F.E. (2010), "Methodology for optimum sensor locations for parameter identification in dynamic systems", J. Eng. Mech., 120(2), 368-390. https://doi.org/10.1061/(ASCE)0733-9399(1994)120:2(368)
  33. Waldraff, W., Dochain, D., Bourrel, S. and Magnus, A. (1998), "On the use of observability measures for sensor location in tubular reactor", J. Process Control, 8(5-6), 497-505. https://doi.org/10.1016/S0959-1524(98)00017-1
  34. Wang, M.L., Satpathi, D., Koontz, S., Jarosevic, A. and Chandoga, M. (1999), "Monitoring of cable forces using magneto-elastic sensors", Comput. Mech. Struct. Eng., 337-347.
  35. Yang, Y. and Nagarajaiah, S. (2013), "Blind modal identification of output-only structures in time-domain based on complexity pursuit", Earthq. Eng. Struct. D., 42(13), 1885-1905. https://doi.org/10.1002/eqe.2302
  36. Yang, Y. and Nagarajaiah, S. (2013), "Output-only modal identification with limited sensors using sparse component analysis", J. Sound Vib., 332(19), 4741-4765. https://doi.org/10.1016/j.jsv.2013.04.004
  37. Yang, Y. and Nagarajaiah, S. (2014), "Structural damage identification via a combination of blind feature extraction and sparse representation classification", Mech. Syst. Signal Pr., 45(1), 1-23. https://doi.org/10.1016/j.ymssp.2013.09.009
  38. Yao, L., Sethares, W.A. and Kammer, D.C. (1992), "Sensor placement for on-orbit modal identification of large space structure via a genetic algorithm", Proceedings of the IEEE International Conference on Systems Engineering, Kobe, Japan, September.
  39. Yi, T.H., Li, H.N., Gu, M. and Zhang X.D. (2015), "Sensor placement optimization in structural health monitoring using niching monkey algorithm", Smart Struct. Syst., 15(1), 191-207. https://doi.org/10.12989/sss.2015.15.1.191
  40. Yi, T.H., Zhou, G.D., Li, H.N. and Zhang, X.D. (2015), "Optimal sensor placement for health monitoring of high-rise structure based on collaborative-climb monkey algorithm", Struct. Eng. Mech.s, 54(2), 305-317. https://doi.org/10.12989/sem.2015.54.2.305
  41. Zhou, G.D., Yi, T.H., Zhang, H. and Li, H.N. (2015), "Optimal nsor placement under uncertainties using a nondirective ent glowworm swarm optimization algorithm", Smart Struct. Syst, 16(2), 243-262. https://doi.org/10.12989/sss.2015.16.2.243

Cited by

  1. Structural damage detection with distributed long-gauge FBG sensors under multi-point excitations vol.28, pp.9, 2017, https://doi.org/10.1088/1361-665x/ab28e6