Smart Structures and Systems

  • 제28권2호
  • /
  • Pages.261-273
  • /
  • 2021
  • /
  • 1738-1584(pISSN)
  • /
  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Implementation of online model updating with ANN method in substructure pseudo-dynamic hybrid simulation

  • Wang, Yan Hua (Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University) ;
  • Lv, Jing (Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University) ;
  • Feng, Yan (Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University) ;
  • Dai, Bo Wen (Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University) ;
  • Wang, Cheng (Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University) ;
  • Wu, Jing (Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University) ;
  • Chen, Zi Yan (Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University)
  • 투고 : 2020.10.17
  • 심사 : 2021.04.23
  • 발행 : 2021.08.25
⟨ 이전 논문 다음 논문 ⟩

초록

Substructure pseudo-dynamic hybrid simulation (SPDHS) is an advanced structural seismic testing method which combines physical experiment and numerical simulation. Generally, the key components which display nonlinearity first are taken as experimental substructures for actual test, and the remaining parts are modeled in simulation. Model updating techniques can be effectively applied to enhance the model precision of nonlinear numerical elements. Specifically, the constitutive model of the experimental substructure is identified online by the instantaneously-measured data, and the corresponding numerical elements with similar hysteretic behaviors are updated synchronously. Artificial neural network (ANN) can recognize the system which cannot be represented by definite numerical model, and thus avoids the structural response distortion caused by the inherent numerical model defects. In this study, a framework for online model updating in SPDHS with ANN method is expanded to implement actual test validation. Moreover, the effectiveness of ANN method is demonstrated by practical tests of a two-story frame model with bending dampers. Additionally, the unscented Kalman filter technique and offline ANN identification approach are both examined in the test validation. The experimental results show that, under the identical loading history, the online ANN method can significantly reduce the model errors and improve the accuracy of SPDHS.

키워드

과제정보

The research described in this paper was financially supported by the National Natural Science Foundation of China under grants No. 51708110.

참고문헌

  1. Baber, T.T. and Noori, M.N. (1985), "Random vibration of degrading, pinching systems", J. Eng. Mech., 111(8), 1010-1026. https://doi.org/10.1061/(ASCE)0733-9399(1985)111:8(1010)
  2. Chuang, M.C., Hsieh, S.H., Tsai, K.C., Li, C.H., Wang, K.J. and Wu, A.C. (2018), "Parameter identification for on-line model updating in hybrid simulations using a gradient-based method", Earthq. Eng. Struct. Dyn., 47(2), 269-293. https://doi.org/10.1002/eqe.2950
  3. Elanwar, H.H. and Elnashai, A.S. (2016), "Framework for online model updating in earthquake hybrid simulations", J. Earthq. Eng., 20(1), 80-100. https://doi.org/10.1080/13632469.2015.1051637
  4. Hashemi, M.J., Masroor, A. and Mosqueda, G. (2014), "Implementation of online model updating in hybrid simulation", Earthq. Eng. Struct. Dyn., 43(3), 395-412. https://doi.org/10.1002/eqe.2350
  5. Kim, J., Ghaboussi, J. and Elnashai, A.S. (2012), "Hysteresis mechanical-informational modeling of bolted steel frame connections", Eng. Struct., 45, 1-11. https://doi.org/10.1016/j.engstruct.2012.06.014
  6. Kwon, O. and Kammula, V. (2013), "Model updating method for substructure pseudo-dynamic hybrid simulation", Earthq. Eng. Struct. Dyn., 42(13), 1971-1984. https://doi.org/10.1002/eqe.2307
  7. More, J.J. (1978), "The Levenberg-Marquardt algorithm: implementation and theory", In: Numerical Analysis, Springer, Berlin, Heidelberg.
  8. Nakashima, M. and Takai, H. (1985), "Use of Substructure Techniques in Pseudo Dynamic Testing", Research Report No. R111; Building Research Institute, Ministry of Construction, Japan.
  9. Nakashima, M., Kato, H. and Takaoka, E. (1992), "Development of real-time pseudo dynamic testing", Earthq. Eng. Struct. Dyn., 21(1), 79-92. https://doi.org/10.1002/eqe.4290210106
  10. Ou, G., Dyke, S.J. and Prakash, A. (2017), "Real time hybrid simulation with online model updating: An analysis of accuracy", Mech. Syst. Signal Proc., 84, 223-240. https://doi.org/10.1016/j.ymssp.2016.06.015
  11. Phillips, B.M. and Spencer Jr, B.F. (2013), "Model-based feedforward-feedback actuator control for real-time hybrid simulation", J. Struct. Eng., 139(7), 1205-1214. https://doi.org/10.1061/(ASCE)ST.1943-541X.0000606
  12. Rumelhart, D.E., Hinton, G.E. and Williams, R.J. (1986), "Learning representations by back-propagating errors", Nature, 323(6088), 533-536. https://doi.org/10.1016/B978-1-4832-1446-7.50035-2
  13. Schellenberg, A., Kim, H.K. and Takahashi, Y. (2009), OpenFRESCO Command Language Manual, University of California, Berkeley, CA, USA.
  14. Shao, X., Mueller, A. and Mohammed, B.A. (2016), "Real-Time Hybrid Simulation with Online Model Updating: Methodology and Implementation", J. Eng. Mech., 142(2), 04015074. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000987
  15. Spencer, B.F., Chang, C.M., Frankie, T.M., Kuchma, D.A., Silva, P.F. and Abdelnaby, A.E. (2014), "A phased approach to enable hybrid simulation of complex structures", Earthq. Eng. Eng. Vib., 13(1), 63-77. https://doi.org/10.1007/s11803-014-0240-2
  16. Tang, Z., Dietz, M., Hong, Y. and Li, Z. (2020), "Performance extension of shaking table-based real-time dynamic hybrid testing through full state control via simulation", Struct. Control. Health. Monitor., 27(10), e2611. https://doi.org/10.1002/stc.2611
  17. Wang, T., Cheng, C. and Guo, X. (2012), "Model-based predicting and correcting algorithms for substructure online hybrid tests", Earthq. Eng. Struct. Dyn., 41(15), 2331-2349. https://doi.org/10.1002/eqe.2190
  18. Wang, T., Zhai, X.H., Meng, L.Y. and Wang, Z. (2017a), "Hybrid testing method based on an online neural network algorithm", J. Vib. Shock, 36(14), 1-8. [In Chinese] https://doi.org/10.13465/j.cnki.jvs.2017.14.001
  19. Wang, T., Zhai, X.H. and Meng, L.Y. (2017b), "An online adaptive neural network algorithm and its parameters robustness analysis", J. Vib. Shock, 38(8), 210-217. [In Chinese] https://doi.org/CNKI:SUN:ZDCJ.0.2019-08-032
  20. Wang, Y.H., Lv, J., Wu, J. and Wang, C. (2020), "ANN based on forgetting factor for online model updating in substructure pseudo-dynamic hybrid simulation", Smart. Struct. Syst., Int. J., 26(1), 63-75. https://doi.org/10.12989/sss.2020.26.1.063
  21. Wu, B. and Wang, T. (2014), "Model updating with constrained unscented kalman filter for hybrid testing", Smart. Struct. Syst., Int. J., 14(6), 1105-1129. https://doi.org/10.12989/sss.2014.14.6.1105
  22. Wu, B., Chen, Y., Xu, G., Mei, Z., Pan, T. and Zeng, C. (2016), "Hybrid simulation of steel frame structures with sectional model updating", Earthq. Eng. Struct. Dyn., 45(8), 1251-1269. https://doi.org/10.1002/eqe.2706
  23. Wu, B., Ning, X., Xu, G., Wang, Z., Mei, Z. and Yang, G. (2018), "Online numerical simulation: A hybrid simulation method for incomplete boundary conditions", Earthq. Eng. Struct. Dyn., 47(4), 889-905. https://doi.org/10.1002/eqe.2996
  24. Yang, W.J. and Nakano, Y. (2005), "Substructure online test by using real-time hysteresis modeling with a neural network", Adv. Experim. Struct. Eng., 38, 267-274.
  25. Yang, Y.S., Tsai, K.C., Elnashai, A.S. and Hsieh, T.J. (2012), "An online optimization method for bridge dynamic hybrid simulations", Simul. Model. Pract. Theory, 28, 42-54. https://doi.org/10.1016/j.simpat.2012.06.002
  26. Yun, G.J., Ghaboussi, J. and Elnashai, A.S. (2008a), "A new neural network-based model for hysteresis behavior of materials", Int. J. Numer. Meth. Eng., 73(4), 447-469. https://doi.org/10.1002/nme.2082
  27. Yun, G.J., Ghaboussi, J. and Elnashai, A.S. (2008b), "Self-learning simulation method for inverse nonlinear modeling of cyclic behavior of connections", Comput. Meth. Appl. Mech. Eng., 197(33-40), 2836-2857. https://doi.org/10.1016/j.cma.2008.01.021