Smart Structures and Systems

  • 제15권5호
  • /
  • Pages.1329-1344
  • /
  • 2015
  • /
  • 1738-1584(pISSN)
  • /
  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Repetitive model refinement for structural health monitoring using efficient Akaike information criterion

  • Lin, Jeng-Wen (Department of Civil Engineering, Feng Chia University)
  • 투고 : 2014.01.14
  • 심사 : 2014.06.29
  • 발행 : 2015.05.25
⟨ 이전 논문 다음 논문 ⟩

초록

The stiffness of a structure is one of several structural signals that are useful indicators of the amount of damage that has been done to the structure. To accurately estimate the stiffness, an equation of motion containing a stiffness parameter must first be established by expansion as a linear series model, a Taylor series model, or a power series model. The model is then used in multivariate autoregressive modeling to estimate the structural stiffness and compare it to the theoretical value. Stiffness assessment for modeling purposes typically involves the use of one of three statistical model refinement approaches, one of which is the efficient Akaike information criterion (AIC) proposed in this paper. If a newly added component of the model results in a decrease in the AIC value, compared to the value obtained with the previously added component(s), it is statistically justifiable to retain this new component; otherwise, it should be removed. This model refinement process is repeated until all of the components of the model are shown to be statistically justifiable. In this study, this model refinement approach was compared with the two other commonly used refinement approaches: principal component analysis (PCA) and principal component regression (PCR) combined with the AIC. The results indicate that the proposed AIC approach produces more accurate structural stiffness estimates than the other two approaches.

키워드

참고문헌

  1. Abdul-Wahab,S.A., Bakheit,C.S. and Al-Alawi, S.M. (2005), "Principal component and multiple regression analysis in modelling of ground-level ozone and factors affecting its concentrations", Environ. Modell. Softw., 20(10), 1263-1271. https://doi.org/10.1016/j.envsoft.2004.09.001
  2. Ahin, A.S., Bayraktar, A., O zcan, D.M., Sevim, B., Altunisik, A.C. and Turker, T. (2011), "Dynamic field test, system identification, and modal validation of an RC minaret: preprocessing and post processing the wind-induced ambient vibration data", J. Perform. Constr. Fac., 25(4), 336-356. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000167
  3. Bennett, P.J., Soga, K., Wassell, I., Fidler, P., Abe, K., Kobayashi, Y. and Vanicek, M. (2010), "Wireless sensor networks for underground railway applications: case studies in Prague and London", Smart Struct. Syst., 6(5-6), 619-639. https://doi.org/10.12989/sss.2010.6.5_6.619
  4. Boukhatem, B., Kenai, S., Hamou, A.T., Ziou D.J. and Ghrici, M. (2012), "Predicting concrete properties using neural networks (NN) with principal component analysis (PCA) technique", Comput. Concr., 10(6), 557-573. https://doi.org/10.12989/cac.2012.10.6.557
  5. Chan, C.M., Tang, S.K., Wong, H. and Lee, W.L. (2012), "On weak unsteady signal detection using statistical tests", Appl. Acoust., 73(2), 164-172. https://doi.org/10.1016/j.apacoust.2011.06.018
  6. Chang, P.C., Flatau, A. and Liu, S.C. (2003), "Review paper: health monitoring of civil infrastructure", Struct. Health Monit., 2(3), 257-267. https://doi.org/10.1177/1475921703036169
  7. Depczynski, U., Frost, V.J. and Molt, K. (2000), "Genetic algorithms applied to the selection of factors in principal component regression", Anal. Chimica Acta, 420(2), 217-227. https://doi.org/10.1016/S0003-2670(00)00893-X
  8. Figueiredo, E., Figueiras, J., Park, G., Farrar, C.R. and Worden, K.(2011), "Influence of the autoregressive model order on damage detection", Comput. Aided Civil Infrastruct. Eng., 26(3), 225-238. https://doi.org/10.1111/j.1467-8667.2010.00685.x
  9. Harada, T., Ariyoshi, N., Shimura, H., Sato, Y., Yokoyama, I., Takahashi, K., Yamagata, S.I., Imamaki, M., Kobayashi, Y., Ishii, I., Miyazaki, M. and Kitada, M. (2010), "Application of Akaike information criterion to evaluate warfarin dosing algorithm", Thrombosis Res., 126, 183-190. https://doi.org/10.1016/j.thromres.2010.05.016
  10. Ho, D.D., Lee, P.Y., Nguyen, K.D., Hong, D.S., Lee, S.Y., Kim, J.T. and Shinozuka, M. (2012). "Solar-powered multi-scale sensor node on Imote2 platform for hybrid SHM in cable-stayed bridge", Smart Struct. Syst., 9(2), 145-164. https://doi.org/10.12989/sss.2012.9.2.145
  11. Huang, N.E., Shen, Z., Long, S.R. Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.C., Tung, C.C. and Liu, H.H.(1998), "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis", Procedures of the Roy. Soc. London, 454(1971), 903-995. https://doi.org/10.1098/rspa.1998.0193
  12. Hutter, M. and Tran, M.N. (2010), "Model selection with the loss rank principle", Comput. Statist. Data An., 54(5), 1288-1306. https://doi.org/10.1016/j.csda.2009.11.015
  13. Jamoos, A., Grivel, E., Shakarneh, N. and Abdel-Nour, H. (2010), "Dual optimal filters for parameter estimation of a multivariate autoregressive process from noisy observations", Signal Process., 5(5), 471-479. https://doi.org/10.1016/0165-1684(83)90017-8
  14. Jolliffe, I.T. (1986), Principal Component Analysis, second edition, Springer, New York, NY, USA.
  15. Jo, H., Park, J.W., Spencer, B.F., Jr. and Jung, H.J. (2013), "Development of high-sensitivity wireless strain sensor for structural health monitoring", Smart Struct. Syst., 11(5), 477-496. https://doi.org/10.12989/sss.2013.11.5.477
  16. Kim, S., Torbol, M. and Chou, P.H. (2013), "Remote structural health monitoring systems for next generation SCADA", Smart Struct. Syst., 11(5), 511-531. https://doi.org/10.12989/sss.2013.11.5.511
  17. Lautour, O.R. and Omenzetter, P. (2010), "Damage classification and estimation in experimental structures using time series analysis and pattern recognition", Mech. Syst. Signal Pr., 24(5), 1556-1569. https://doi.org/10.1016/j.ymssp.2009.12.008
  18. Li, H.G. and Meng, G. (2007), "Nonlinear dynamics of a SDOF oscillator with Bouc-Wen hysteresis", Solitons Fract., 34(2), 337-343. https://doi.org/10.1016/j.chaos.2006.03.081
  19. Lin, J.W., Betti, R., Smyth, A.W. and Longman, R.W. (2001), "On-line identification of non-linear hysteretic structural systems using a variable trace approach", Earthq. Eng. Struct. D., 30(9), 1279-1303. https://doi.org/10.1002/eqe.63
  20. Lin, J.W. and Chen, H.J. (2009), "Repetitive identification of structural systems using a nonlinear model parameter refinement approach", Shock Vib., 16, 229-240. https://doi.org/10.1155/2009/174917
  21. Lin, J.W., Chen, C.W. and Hsu, T.C. (2013), "A novel regression prediction model for structural engineering applications", Struct. Eng. Mech., 45(5), 693-702. https://doi.org/10.12989/sem.2013.45.5.693
  22. Lin, J.W. (2011), "A hybrid algorithm based on EEMD and EMD for multi-mode signal processing", Struct. Eng. Mech., 39(6), 813-831. https://doi.org/10.12989/sem.2011.39.6.813
  23. Lin, J.W., Chen, C.W. and Chung, S.H. (2012), "Modeling and assessment of bridge structure for seismic hazard prevention", Natural Hazards, 61(3), 1115-1126. https://doi.org/10.1007/s11069-011-9969-3
  24. Lin, J.W. (2010), "Mode-by-mode evaluation of structural systems using a bandpass-HHT filtering approach", Struct. Eng. Mech., 36(6), 697-714. https://doi.org/10.12989/sem.2010.36.6.697
  25. Ljung, L. (1999), System Identification: Theory for the User, Prentice-Hal PTR, New Jersey.
  26. Lu, K.C., Loh, C.H., Yang, Y.S., Lynch, J.P. and Law, K.H. (2008), "Real-time structural damage detection using wireless sensing and monitoring system", Smart Struct. Syst., 4(6), 759-77. https://doi.org/10.12989/sss.2008.4.6.759
  27. Masri, S.F., Smyth, A.W., Chassiakos, A.G., Caughey, T.K. and Hunter, N.F. (2000), "Application of neural networks for detection of changes in nonlinear systems", J. Eng. Mech. - ASCE, 126(7), 666-676. https://doi.org/10.1061/(ASCE)0733-9399(2000)126:7(666)
  28. McAdams, H.T., Crawford, R.W. and Hadder, G.R. (2000), A vector approach to regression analysis and its application to heavy-duty diesel emissions, Society of Automotive Engineers, Inc. 2000-01-1961.
  29. Meo, M., Zumpano, G., Meng, X., Cosser, E., Roberts, G. and Dodson, A. (2006), "Measurements of dynamic properties of a medium span suspension bridge by using the wavelet transforms", Mech. Syst. Signal Pr., 20(5), 1112-1133. https://doi.org/10.1016/j.ymssp.2004.09.008
  30. Milenkovic, P. (2011), "Solution of the forward dynamics of a single-loop linkage using power series", J. Dynam. Syst. Measurement Control, 133(6), 061002 (9 pages). https://doi.org/10.1115/1.4004766
  31. Nagayama, T., Sim, S.H., Miyamori, Y. and Spencer, B.F. (2007), "Issues in structural health monitoring employing smart sensors", Smart Struct. Syst., 3(3), 299-320. https://doi.org/10.12989/sss.2007.3.3.299
  32. Park, B., Chen, Y.R., Hruschka, W.R., Shackelford, S.D. and Koohmaraie, M. (2001), "Principal component regression of near-infrared reflectance spectra for beef tenderness prediction", T. Am. Soc. Agricultural Engineers, 44, 609-615.
  33. Pei, J.S., Smyth, A.W. and Kosmatopoulos, E.B. (2004), "Analysis and modification of Volterra/Wiener neural networks for the adaptive identification of non-linear hysteretic dynamic systems", J. Sound Vib., 275(3-5), 693-718. https://doi.org/10.1016/j.jsv.2003.06.005
  34. Posada, D. and Buckley, T.R. (2004), "Model selection and model averaging in phylogenetics: advantages of Akaike information criterion and Bayesian approaches over likelihood ratio tests", Systematic Biologists, 53(5), 793-808. https://doi.org/10.1080/10635150490522304
  35. Rogers, B.P., Katwal, S.B., Morgan, V.L., Asplund, C.L. and Gore, J.C. (2010), "Functional MRI and multivariate autoregressive models", Mag. Reson. Imaging, 28(8), 1058-1065. https://doi.org/10.1016/j.mri.2010.03.002
  36. Sousa, S.I.V., Martins, F.G., Alvim-Ferraz, M.C.M. and Pereira, M.C. (2007), "Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations", Environ. Modell. Softw., 22(1), 97-103. https://doi.org/10.1016/j.envsoft.2005.12.002
  37. Statheropoulos, M., Vassiliadis, N. and Pappa, A. (1998), "Principal component and canonical correlation analysis for examining air pollution and metrological data", Atmos. Environ., 32(6), 1087-1095. https://doi.org/10.1016/S1352-2310(97)00377-4
  38. Sumitro, S. and Wang, M.L. (2005), "Sustainable structural health monitoring system", Struct. Health Monit., 12(3-4), 445-467. https://doi.org/10.1002/stc.79
  39. Taylor, S.G., Farinholt, K.M., Park, G., Todd, M.D. and Farrar, C.R. (2010), "Multi-scale wireless sensor node for health monitoring of civil infrastructure and mechanical systems", Smart Struct. Syst., 6(5-6), 661-673. https://doi.org/10.12989/sss.2010.6.5_6.661
  40. Tsai, C.S. (1996), Nonlinear Stress Analysis Technique-NSAT, Feng Chia University, Taiwan, R.O.C.
  41. Wada, M., Ogawa, T., Sonoda, H. and Sato, K. (1996), "Development of relative power contribution ratio of the EEG in normal children: a multivariate autoregressive modeling approach", Electroencephalography Clinical Neurophysiology, 98(1), 69-75. https://doi.org/10.1016/0013-4694(95)00187-5
  42. Weng, J.H., Loh, C.H., Lynch, J.P., Lu, K.C., Lin, P.Y. and Wang, Y. (2008), "Output-only modal identification of a cable-stayed bridge using wireless monitoring systems", Eng. Struct., 30(7), 1820-1830. https://doi.org/10.1016/j.engstruct.2007.12.002
  43. Wong, S.C. and Barhorst, A.A. (2006), "Polynomial interpolated Taylor series method for parameter identification of nonlinear dynamic system", J. Comput. Nonlinear Dynam., 1,248-256. https://doi.org/10.1115/1.2209647
  44. Xia, B., Chan, A.P.C. and Yeung, J.F.Y. (2011), "Developing a fuzzy multicriteria decision-making model for selecting design-build operational variations", J. Constr. Eng. M. - ASCE, 137(12), 1176-1184. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000381

피인용 문헌

  1. Micro Radar Surface Velocimetry for Hydrologic Signal Processing Using a Bandpass Filtering Approach vol.8, pp.12, 2016, https://doi.org/10.3390/w8060262