Earthquakes and Structures

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Real-time seismic structural response prediction system based on support vector machine

  • Lin, Kuang Yi (Department of Civil Engineering, National Chiao Tung University) ;
  • Lin, Tzu Kang (Department of Civil Engineering, National Chiao Tung University) ;
  • Lin, Yo (Department of Civil Engineering, National Chiao Tung University)
  • Received : 2019.06.17
  • Accepted : 2019.11.26
  • Published : 2020.02.25
⟨ Previous Next ⟩

Abstract

Floor acceleration plays a major role in the seismic design of nonstructural components and equipment supported by structures. Large floor acceleration may cause structural damage to or even collapse of buildings. For precision instruments in high-tech factories, even small floor accelerations can cause considerable damage in this study. Six P-wave parameters, namely the peak measurement of acceleration, peak measurement of velocity, peak measurement of displacement, effective predominant period, integral of squared velocity, and cumulative absolute velocity, were estimated from the first 3 s of a vertical ground acceleration time history. Subsequently, a new predictive algorithm was developed, which utilizes the aforementioned parameters with the floor height and fundamental period of the structure as the new inputs of a support vector regression model. Representative earthquakes, which were recorded by the Structure Strong Earthquake Monitoring System of the Central Weather Bureau in Taiwan from 1992 to 2016, were used to construct the support vector regression model for predicting the peak floor acceleration (PFA) of each floor. The results indicated that the accuracy of the predicted PFA, which was defined as a PFA within a one-level difference from the measured PFA on Taiwan's seismic intensity scale, was 96.96%. The proposed system can be integrated into the existing earthquake early warning system to provide complete protection to life and the economy.

Keywords

Acknowledgement

Supported by : Ministry of Science and Technology

References

  1. Bose, M., Heaton, T. and Hauksson, E. (2012), "Rapid estimation of earthquake source and ground-motion parameters for earthquake early warning using data from a single three-component broadband or strong-motion sensor", Bull. Seismol. Soc. Am., 102(2), 738-750. https://doi.org/10.1785/0120110152.
  2. Calvi, P.M. and Sullivan, T.J. (2014), "Estimating floor spectra in multiple degree of freedom systems", Earthq. Struct., 7(1), 17-38. https://doi.org/10.12989/eas.2014.7.1.017.
  3. Fletcher, R. (2013), Practical Methods of Optimization, John Wiley and Sons, New Jersey, U.S.A.
  4. Harris, H.G. and Sabnis, G. (1999), Structural Modeling and Experimental Techniques, CRC press, Florida, U.S.A.
  5. Hsu, T.Y., Huang, S.K., Chang, Y.W., Kuo, C.H., Lin, C.M., Chang, T.M., and Loh, C.H. (2013), "Rapid on-site peak ground acceleration estimation based on support vector regression and P-wave features in Taiwan", Soil Dyn. Earthq. Eng., 49, 210-217. https://doi.org/10.1016/j.soildyn.2013.03.001.
  6. Kanamori, H. (2005), "Real-time seismology and earthquake damage mitigation", Annu. Rev. Earth Plan. Sci., 33, 195-214. https://doi.org/10.1146/annurev.earth.33.092203.122626
  7. Kanee, A.R.T., Kani, I.M.Z. and Noorzad, A. (2013), "Elastic floor response spectra of nonlinear frame structures subjected to forward-directivity pulses of near-fault records", Earthq. Struct., 5(1), 49-65. https://doi.org/10.12989/eas.2013.5.1.049.
  8. Kubo, T., Hisada, Y., Murakami, M., Kosuge, F. and Hamano, K. (2011), "Application of an earthquake early warning system and a real-time strong motion monitoring system in emergency response in a high-rise building", Soil Dyn. Earthq. Eng., 31(2), 231-239. https://doi.org/10.1016/j.soildyn.2010.07.009.
  9. Lin, T.K., and Wu, C.H. (2017), "Estimation of peak floor acceleration based on support vector regression and p-wave features", MATEC Web of Conferences, 119. https://doi.org/10.1051/matecconf/201711901028.
  10. Loi, D.W., Raghunandan, M.E. and Swamy, V. (2016), "Seismicity of peninsular Malaysia due to intraplate and far field sources", Earthq. Struct., 10(6), 1391-1404. https://doi.org/10.12989/eas.2016.10.6.1391.
  11. Rodriguez, M.E., Restrepo, J.I. and Carr, A.J. (2002), "Earthquake-induced floor horizontal accelerations in buildings", Earthq. Eng. Struct. D., 31(3), 693-718. https://doi.org/10.1002/eqe.149.
  12. Satriano, C., Wu, Y.M., Zollo, A. and Kanamori, H. (2011), "Earthquake early warning: Concepts, methods and physical grounds", Soil Dyn. Earthq. Eng., 31(2), 106-118. https://doi.org/10.1016/j.soildyn.2010.07.007.
  13. Scholkopf, B., Smola, A.J., Williamson, R.C. and Bartlett, P.L. (2000), "New support vector algorithms", Neural Cpmput., 12(5), 1207-1245. https://doi.org/10.1162/089976600300015565.
  14. Takabatake, H. and Ikarashi, F. (2013), "New vibration control device and analytical method for slender structures", Earthq. Struct., 4(1), 11-39. https://doi.org/10.12989/eas.2013.4.1.011.
  15. Tsai, K.C., Lin, P.Y., Lin, C.C.J., Lin, T.K., Lin, Y.B., Lin, J.L., and Chung, C.S. (2010), "Preliminary study of added-value information and analysis for earthquake early warning system", Nat. Center Res. Earthq. Eng., 9-13.
  16. Vapnik, V. (2013), The Nature of Statistical Learning Theory, Springer science and business media, Berlin, Germany.
  17. Wu, Y.M. and Kanamori, H. (2005), "Rapid assessment of damage potential of earthquakes in Taiwan from the beginning of P waves", Bull. Seismol. Soc. Am., 95(3), 1181-1185. https://doi.org/10.1785/0120040193.
  18. Wu, Y.M. and Li, Zhao (2006), "Magnitude estimation using the first three seconds P-wave amplitude in earthquake early warning", Geophys. Res. Lett., 33(16). https://doi.org/10.1029/2006GL026871.
  19. Zhang, X., Liang, D., Zeng, J. and Lu, J. (2014), "SVR model reconstruction for the reliability of FBG sensor network based on the CFRP impact monitoring", Smart Struct. Syst., 14(2), 145-158. https://doi.org/10.12989/sss.2014.14.2.145.

Cited by

  1. Reliability-based assessment of high-speed railway subgrade defect vol.77, pp.2, 2021, https://doi.org/10.12989/sem.2021.77.2.231
  2. Assessment and Localization of Structural Damage in r/c Structures through Intelligent Seismic Signal Processing vol.35, pp.9, 2021, https://doi.org/10.1080/08839514.2021.1935589