Structural Engineering and Mechanics

  • 제80권6호
  • /
  • Pages.711-726
  • /
  • 2021
  • /
  • 1225-4568(pISSN)
  • /
  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Multiparameter recursive reliability quantification for civil structures in meteorological disasters

  • Wang, Vincent Z. (College of Engineering and Science, Victoria University) ;
  • Fragomeni, Sam (College of Engineering and Science, Victoria University)
  • 투고 : 2019.09.24
  • 심사 : 2021.09.13
  • 발행 : 2021.12.25
⟨ 이전 논문 다음 논문 ⟩

초록

This paper presents a multiple parameters-based recursive methodology for the reliability quantification of civil structures subjected to meteorological disasters. Recognizing the challenge associated with characterizing at a single stroke all the meteorological disasters that may hit a structure during its service life, the proposed methodology by contrast features a multiparameter recursive mechanism to describe the meteorological demand of the structure. The benefit of the arrangements is that the essentially inevitable deviation of the practically observed meteorological data from those in the existing model can be mitigated in an adaptive manner. In particular, the implications of potential climate change to the relevant reliability of civil structures are allowed for. The application of the formulated methodology of recursive reliability quantification is illustrated by first considering the reliability quantification of a linear shear frame against simulated strong wind loads. A parametric study is engaged in this application to examine the effect of some hyperparameters in the configured hierarchical model. Further, the application is extended to a nonlinear hysteretic shear frame involving some field-observed cyclone data, and the incompleteness of the relevant structural diagnosis data that may arise in reality is taken into account. Also investigated is another application scenario where the reliability of a building envelope is assessed under hailstone impacts, and the emphasis is to demonstrate the recursive incorporation of newly obtained meteorological data.

키워드

참고문헌

  1. Andrews, K.E. and Blong, R.J. (1997), "March 1990 hailstorm damage in Sydney, Australia", Nat. Hazard., 16(2), 113-125. https://doi.org/10.1023/A:1007913508192.
  2. Blong, R. (2004), "Residential building damage and natural perils: Australian examples and issues", Build. Res. Inform., 32(5), 379-390. https://doi.org/10.1080/0961321042000221007.
  3. Brown, T.M., Pogorzelski, W.H. and Giammanco, I.M. (2015), "Evaluating hail damage using property insurance claims data", Weather Clim. Soc., 7(3), 197-210. https://doi.org/10.1175/WCAS-D-15-0011.1.
  4. Bureau of Meteorology, Australia (2017), Climate Data Online. http://www.bom.gov.au/climate/data.
  5. Chan, T. and Thambiratnam, D.P. (2011), Structural Health Monitoring in Australia, Nova Science Publishers, Hauppauge, NY, USA.
  6. Chang, F.K. and Kopsaftopoulos F. (2015), Proceedings of the 10th International Workshop on Structural Health Monitoring, DEStech Publications, Lancaster, PA.
  7. DiazDelaO, F.A. and Adhikari, S. (2012), "Bayesian assimilation of multi-fidelity finite element models", Comput. Struct., 92-93, 206-215. https://doi.org/10.1016/j.compstruc.2011.11.002.
  8. Figueiredo, E., Radu, L., Worden, K. and Farrar, C.R. (2014), "A Bayesian approach based on a Markov-chain Monte Carlo method for damage detection under unknown sources of variability", Eng. Struct., 80, 1-10. https://doi.org/10.1016/j.engstruct.2014.08.042.
  9. Gelman, A., Carlin, J.B., Stern, H.S. and Rubin, D.B. (2004), Bayesian Data Analysis, 2nd Edition, Chapman & Hall/CRC, Boca Raton, FL, USA.
  10. Haukaas, T. and Gardoni, P. (2011), "Model uncertainty in finite-element analysis: Bayesian finite elements", J. Eng. Mech., ASCE, 137(8), 519-526. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000253.
  11. Highland, L.M. and Bobrowsky, P. (2008), The Landslide Handbook-A Guide to Understanding Landslides, U.S. Geological Survey Circular 1325, U.S. Geological Survey, Reston, VA.
  12. Kim, J. and LaFave, J.M. (2008), "Probabilistic joint shear strength models for design of RC beam-column connections", ACI Struct. J., 105(6), 770-780. https://doi.org/10.14359/20105.
  13. Lee, S.J., Zi, G., Mun, S., Kong, J.S. and Choi, J.H. (2015), "Probabilistic prognosis of fatigue crack growth for asphalt concretes", Eng. Fract. Mech., 141, 212-229. https://doi.org/10.1016/j.engfracmech.2015.04.033.
  14. Maljaars, J. and Vrouwenvelder, T. (2014), "Fatigue failure analysis of stay cables with initial defects: Ewijk bridge case study", Struct. Saf., 51, 47-56. https://doi.org/10.1016/j.strusafe.2014.05.007.
  15. McLachlan, G.J. and Krishnan, T. (2008), The EM Algorithm and Extensions, 2nd Edition, John Wiley & Sons, Hoboken, NJ, USA.
  16. Mostaghel, N. and Byrd, R.A. (2000), "Analytical description of multidegree bilinear hysteretic system", J. Eng. Mech., ASCE, 126(6), 588-598. https://doi.org/10.1061/(ASCE)0733-9399(2000)126:6(588).
  17. Naeim, F. (2001), The Seismic Design Handbook, 2nd Edition, Kluwer Academic Publishers, Norwell, MA, USA.
  18. Novo, A.A. and Schafer, J.L. (2013), norm: Analysis of multivariate normal datasets with missing values, R Package Version 1.0-9.5. http://CRAN.R-project.org/package=norm.
  19. Paterson, D.A. and Sankaran, R. (1994), "Hail impact on building envelopes", J. Wind Eng. Ind. Aerodyn., 53(1-2), 229-246. https://doi.org/10.1016/0167-6105(94)90028-0.
  20. Prabhu, S.R., Lee, Y.J. and Park, Y.C. (2019), "A new Bayesian approach to derive Paris' law parameters from S-N curve data", Struct. Eng. Mech., 69(4), 361-369. https://doi.org/10.12989/sem.2019.69.4.361.
  21. R Core Team (2015), R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org.
  22. Simiu, E. and Scanlan, R.H. (1996), Wind Effects on Structures: Fundamentals and Applications to Design, 3rd Edition, John Wiley & Sons, New York, NY, USA.
  23. Simoen, E., Moaveni, B., Conte, J.P. and Lombaert, G. (2013), "Uncertainty quantification in the assessment of progressive damage in a 7-story full-scale building slice", J. Eng. Mech., ASCE, 139(12), 1818-1830. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000610.
  24. Soong, T.T. and Grigoriu, M. (1993), Random Vibration of Mechanical and Structural Systems, Prentice Hall, Englewood Cliffs, NJ, USA.
  25. Stewart, M.G. and O'Connor, A. (2012), "Probabilistic risk assessment and service life performance management of load bearing biomedical implants", Reliab. Eng. Syst. Saf., 108, 49-55. https://doi.org/10.1016/j.ress.2012.06.012.
  26. Sun, J., Lam, N., Zhang, L., Ruan, D. and Gad, E. (2015), "Contact forces generated by hailstone impact", Int. J. Impact Eng., 84, 145-158. https://doi.org/10.1016/j.ijimpeng.2015.05.015.
  27. Wang, V.Z. and Fragomeni, S. (2015), "Fragility analysis of an in-service hysteretic reinforced concrete system subjected to seismic actions", Proceedings of the 2015 World Congress on Advances in Structural Engineering and Mechanics, Incheon, South Korea, August.
  28. Wang, V.Z. and Ginger, J.D. (2014), "Maximum a posteriori estimation based wind fragility analysis with application to existing linear or hysteretic shear frames", Struct. Eng. Mech., 50(5), 653-664. https://doi.org/10.12989/sem.2014.50.5.653.
  29. Wang, V.Z., Pease, T. and Robinson, S. (2016), "Statistical damage prognosis for in-service civil structures against hazards: Formulations and applications", J. Eng. Mech., ASCE, 142(3), 04015090. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000969.
  30. Xu, Y.L., Zhu, S., Xia, Y., Ni, Y.Q., Law, S.S., Yin, J.H. and Su, Z.Q. (2013), Proceedings of the 6th International Conference on Structural Health Monitoring of Intelligent Infrastructure, Hong Kong Polytechnic University, Hong Kong, China.