Earthquakes and Structures

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Bayesian approach for the accuracy evaluating of the seismic demand estimation of SMRF

  • Ayoub Mehri Dehno (Department of Civil Engineering, Qazvin Branch, Islamic Azad University) ;
  • Hasan Aghabarati (Department of Civil Engineering, Qazvin Branch, Islamic Azad University) ;
  • Mehdi Mahdavi Adeli (Department of Civil Engineering, Shoushtar Branch, Islamic Azad University)
  • Received : 2023.08.24
  • Accepted : 2024.01.09
  • Published : 2024.02.25
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Abstract

Probabilistic model of seismic demand is the main tool used for seismic demand estimation, which is a fundamental component of the new performance-based design method. This model seeks to mathematically relate the seismic demand parameter and the ground motion intensity measure. This study is intended to use Bayesian analysis to evaluate the accuracy of the seismic demand estimation of Steel moment resisting frames (SMRFs) through a completely Bayesian method in statistical calculations. In this study, two types of intensity measures (earthquake intensity-related indices such as magnitude and distance and intensity indices related to ground motion and spectral response including peak ground acceleration (PGA) and spectral acceleration (SA)) have been used to form the models. In addition, an extensive database consisting of sixty accelerograms was used for time-series analysis, and the target structures included five SMRFs of three, six, nine, twelve and fifteen stories. The results of this study showed that for low-rise frames, first mode spectral acceleration index is sufficient to accurately estimate demand. However, for high-rise frames, two parameters should be used to increase the accuracy. In addition, adding the product of the square of earthquake magnitude multiplied by distance to the model can significantly increase the accuracy of seismic demand estimation.

Keywords

References

  1. Amini, A., Abdollahi, A., Hariri-Ardebili, M. and Lall, U. (2021), "Copula-based reliability and sensitivity analysis of aging dams: Adaptive Kriging and polynomial chaos Kriging methods", Appl. Soft Comput., 109, 107524. https://doi.org/10.1016/j.asoc.2021.107524. 
  2. BSS Council (2009), NEHRP Recommended Seismic Provisions for New Buildings and Other Structures (FEMA P-750), Federal Emergency Management Agency, Washington, D.C., USA.
  3. Bai, J.W., Gardoni, P. and Hueste, M.B.D. (2011), "Storey-specific demand models and seismic fragility estimates for multi-storey buildings", Struct. Saf., 33(1), 96-107. https://doi.org/10.1016/j.strusafe.2010.09.002. 
  4. Baker, J.W. and Cornell, C.A. (2008), "Uncertainty propagation in probabilistic seismic loss estimation", Struct. Saf., 30(3), 236-252. https://doi.org/10.1016/j.strusafe.2006.11.003. 
  5. Basereh, S. (2021), "Seismic retrofit of reinforced concrete shear walls using selective weakening and self-centering", Ph.D. Thesis, State University of New York, Buffalo, NY, USA.
  6. Bazzurro, P. (1998), "Probabilistic seismic demand analysis", Ph.D. Thesis, Stanford University, Stanford, CA, USA.
  7. Bozorgnia, Y. and Bertero, V.V. (2001), "Improved shaking and damage parameters for post-earthquake applications", Proceedings of Strong Motion Instrumentation Program (SMIP01) Seminar on Utilization of Strong-Motion Data, California Division of Mines and Geology, Los Angeles, CA, USA, September. 
  8. Bradley, B.A. (2013), "A critical examination of seismic response uncertainty analysis in earthquake engineering", Earthq. Eng. Struct. Dyn., 42(11), 1717-1729. https://doi.org/10.1002/eqe.2331. 
  9. Brezger, A. and Lang, S. (2006), "Generalized structured additive regression based on Bayesian P-splines", Comput. Stat. Data Anal., 50(4), 967-991. https://doi.org/10.1016/j.csda.2004.10.011. 
  10. Brezger, A. and Lang, S. (2008), "Simultaneous probability statements for Bayesian P-splines'', Statist. Stat. Modell., 8(2), 141-168. https://doi.org/10.1177/1471082X0800800202. 
  11. Carballo, J. and Cornell, C.A. (2000), "Probabilistic seismic demand analysis: Spectrum matching and design", Reliability of Marine Structures Program Report No. RMS-41; Department of Civil and Environmental Engineering, Stanford University, Stanford, CA, USA.
  12. Cornell, C.A. (1996), "Calculating building seismic performance reliability: A basis for multi-level design norms", Proceedings of 11th World Conference on Earthquake Engineering, Acapulco, Mexico, June. 
  13. Cornell, C.A., Jalayer, F., Hamburger, R.O. and Foutch, D.A. (2002), "Probabilistic basis for the 2000 SAC FEMA steel moment frame", J. Struct. Eng., 128(4), 526-533. https://doi.org/10.1061/(ASCE)0733-9445(2002)128:4(526). 
  14. Der Kiureghian, A. (1999), "A Bayesian framework for fragility assessment", Proceedings of 8th International Conference on Applications of Statistics and Probability: Civil Engineering Reliability and Risk Analysis, Sydney, New South Wales, Australia, December. 
  15. Du, A., Padgett, J.E. and Shafieezadeh, A. (2019), "A posteriori optimal intensity measures for probabilistic seismic demand modeling", Bull. Earthq. Eng., 17(2), 681. https://doi.org/10.1007/s10518-018-0484-8. 
  16. Esteghamati, M.Z. and Farzampour, A. (2020), "Probabilistic seismic performance and loss evaluation of a multi-story steel building equipped with butterfly-shaped fuses", J. Constr. Steel Res., 172, 106187. https://doi.org/10.1016/j.jcsr.2020.106187. 
  17. Esteghamati, M.Z. and Flint, M.M. (2021), "Developing datadriven surrogate models for holistic performance-based assessment of mid-rise RC frame buildings at early design", Eng. Struct., 245, 112971. https://doi.org/10.1016/j.engstruct.2021.112971. 
  18. Fadzli, M.N. and Pang, Y.K. (2014), "Seismic performance of moment resisting steel frame subjected to earthquake excitations", Front. Struct. Civil Eng., 8(1), 19-25. https://doi.org/10.1007/s11709-014-0240-3. 
  19. Flenga, M.G. and Favvata, M.J. (2021), "Probabilistic seismic assessment of the pounding risk based on the local demands of a multistory RC frame structure", Eng. Struct., 245, 112789. https://doi.org/10.1016/j.engstruct.2021.112789. 
  20. Gardoni, P., Der Kiureghian, A. and Mosalam, K.M. (2003), "Probabilistic models and fragility estimates for bridge components and systems", Report No. 2003/13 PEER; Univerdity of California, Berkeley, Berkeley, CA, USA.
  21. Ghasemof, A., Mirtaheri, M. and Mohammadi, R.K. (2022), "Effects of demand parameters in the performance-based multiobjective optimum design of steel moment frame buildings", Soil Dyn. Earthq. Eng., 153, 107075. https://doi.org/10.1016/j.soildyn.2021.107075. 
  22. Hai-Bin, M.A.,Wei-Dong, Z., Lavorato, D., Nuti, C., Fiorentino, G., Marano, C., Greco, R. and Briseghella, B. (2019), "Probabilistic seismic response and uncertainty analysis of continuous bridges under near-fault ground motions", Front. Struct. Civil Eng., 13(6), 1510-1519. https://doi.org/10.1007/s11709-019-0577-8. 
  23. Han, S.W. and Wen, Y.K. (1997), "Method of reliability-based seismic design. I: equivalent nonlinear systems'', J. Struct. Eng., 123(3), 256-263. https://doi.org/10.1061/(ASCE)0733-9445(1997)123:3(256). 
  24. Hariri-Ardebili, M. and Saouma, V. (2016), "Probabilistic seismic demand model and optimal intensity measure for concrete dams", Struct. Saf., 59, 67-85. https://doi.org/10.1016/j.strusafe.2015.12.001. 
  25. Harrington, C.C. and Liel, A.B. (2021), "Indicators of improvements in seismic performance possible through retrofit of reinforced concrete frame buildings", Earthq. Spectra, 37(1), 262-283. https://doi.org/10.1177/8755293020936707. 
  26. Ibarra, L.F. and Krawinkler, H. (2005), "Global collapse of frame structures under seismic excitations", Report No. 2005/05 PEER; University of California at Berkeley, Berkeley, CA, USA. 
  27. Jalayer, F. and Cornell, C.A. (2003), "A technical framework for probability-based demand and capacity factor design (DCFD) seismic formats", Report No.2003/08 PEER; University of California at Berkeley, Berkeley, CA, USA.
  28. Javanmard, M. and Yahyaabadi, A. (2019), "Assessment of structure-specific intensity measures for the probabilistic seismic demand analysis of steel moment frames", Arab. J. Sci. Eng., 44, 4885-4894. https://doi.org/10.1007/s13369-018-3584-5. 
  29. Liu, X.X., Wu, Z.Y. and Liang, F. (2016), "Multidimensional performance limit state for probabilistic seismic demand analysis", Bull. Earthq. Eng., 14, 3389-3408. https://doi.org/10.1007/s10518-016-0013-6. 
  30. Lounis, Z. and McAllister, T.P. (2016), "Risk-based decision making for sustainable and resilient infrastructure systems", J. Struct. Eng., 142(9), F4016005. https://doi.org/10.1061/(ASCE)ST.1943-541X.0001545. 
  31. Luco, N. (2002), "Probabilistic seismic demand analysis, SMRF connection fractures, and near source effects", Ph.D. Thesis, Stanford University, Stanford, CA, USA.
  32. Mackie, K. and Stojadinovic, B. (2001), "Probabilistic seismic demand model for California highway bridges", J. Bridge Eng., 6(6), 468-481. https://doi.org/10.1061/(ASCE)1084-0702(2001)6:6(468). 
  33. Mahdavi Adeli, M., Deylami, A. and Banazadeh, M. (2011), "Bayesian approach for determination of drift hazard curves for generic steel moment-resisting frames in territory of Tehran", Int. J. Civil Eng., 9(3), 145-154. 
  34. Mahdavi Adeli, M., Deylami, A., Banazadeh, M. and Alinia, M.M. (2011), "A Bayesian approach to construction of probabilistic seismic demand models for steel moment-resisting frames", Sci. Iran., 18(4), 885-894. https://doi.org/10.1016/j.scient.2011.07.019. 
  35. Mahdavi Adeli, M., Banazadeh, M., Deylami, A. and Alinia, M.M. (2012), "Introducing a new spectral intensity measure parameter to estimate the seismic demand of steel moment-resisting frames using Bayesian statistics", Adv. Struct. Eng., 15(2) 231-247. https://doi.org/10.1260/1369-4332.15.2.231. 
  36. Medina, R.A. and Krawinkler, H. (2005), "Evaluation of drift demands for the seismic performance assessment of frames", J. Struct. Eng., 131(7), 1003-1013. https://doi.org/10.1061/(ASCE)0733-9445(2005)131:7(1003). 
  37. Mieler, M.W. (2012), "Toward resilient communities: A performance-based engineering framework for design and evaluation of the built environment", Ph.D. Thesis, University of California, Berkeley, Berkeley, CA, USA.
  38. Miranda, E., Akkar, S.D. (2006), "Generalized interstorey drift spectrum", J. Struct. Eng., 132(6), 840-852. https://doi.org/10.1061/(ASCE)0733-9445(2006)132:6(840). 
  39. Mitseas, I.P., Kougioumtzoglou, I.A. and Beer, M. (2016), "An approximate stochastic dynamics approach for nonlinear structural system performance-based multi-objective optimum design", Struct. Saf., 60, 67-76. https://doi.org/10.1016/j.strusafe.2016.01.003. 
  40. Moehle, J.P. (1984), "Strong motion drift estimates for R/C structures", J. Struct. Eng., 110(9), 1988-2001. https://doi.org/10.1061/(ASCE)0733-9445(1984)110:9(1988). 
  41. Moehle, J. and Deierlein, G.G. (2004), "A framework methodology for performance-based earthquake engineering", Proceedings of 13th World Conference on Earthquake Engineering, Vancouver, BC, Canada, August. 
  42. Nguyen, D.D., Lee, T.H. and Phan, V.T. (2021), "Optimal earthquake intensity measures for probabilistic seismic demand models of base-isolated nuclear power plant structures", Energies, 14(16), 5163. https://doi.org/10.3390/en14165163. 
  43. OpenSees (2020), Open System for Earthquake Engineering Simulation, Pacific Earthquake Engineering Research Center, University of California, Berkeley, Berkeley, CA, USA.
  44. PEER Center (2020), PEER Ground Motion Database, Pacific Earthquake Engineering Research Center, University of California, Berkeley, Berkeley, CA, USA. https://ngawest2.berkeley.edu/ 
  45. Roohi, M. and Hernandez, E.M. (2020), "Performance-based postearthquake decision making for instrumented buildings", J. Civil Struct. Health Monit., 10(5), 775-792. https://doi.org/10.1007/s13349-020-00416-1. 
  46. Shi, F., Saygili, G., Ozbulut, O.E. and Zhou, Y. (2020), "Riskbased mainshock-aftershock performance assessment of SMA braced steel frames", Eng. Struct., 212, 110506. https://doi.org/10.1016/j.engstruct.2020.110506. 
  47. Shome, N. (1999), "Probabilistic seismic demand analysis of nonlinear structures", Ph.D. Thesis, Stanford University, Stanford, CA, USA. 
  48. Soleimani-Babakamali, M.H. and Zaker Esteghamati, M. (2022), "Estimating seismic demand models of a building inventory from nonlinear static analysis using deep learning methods", Eng. Struct., 266, 114576. https://doi.org/10.1016/j.engstruct.2022.114576. 
  49. Soraghi, A. (2021), "Probabilistic characterization of bond behavior at rebar-concrete interface in corroded RC structures: Experiment, modeling, and implementation", Ph.D. Thesis, University of Akron, Akron, Ohio, USA. 
  50. Sousa, L., Silva, V., Marques, M. and Crowley, H. (2018), "On the treatment of uncertainty in seismic vulnerability and portfolio risk assessment", Earthq. Eng. Structural Dyn., 47(1), 87-104. https://doi.org/10.1002/eqe.2940. 
  51. Tarfan, S., Banazadeh, M. and Esteghamati, M.Z. (2019), "Probabilistic seismic assessment of non-ductile RC buildings retrofitted using pre-tensioned aramid fiber reinforced polymer belts", Compos. Struct., 208, 865-878. https://doi.org/10.1016/j.compstruct.2018.10.048. 
  52. Taslimi, A. and Tehranizadeh, M. (2022), "The effect of vertical near-field ground motions on the collapse risk of high-rise reinforced concrete frame-core wall structures", Adv. Struct. Eng., 25(2), 410-425. https://doi.org/10.1177/136943322110561. 
  53. Tian, L., Pan, P. and Ma, R. (2019), "Probabilistic seismic demand model and fragility analysis of transmission tower subjected to near-field ground motions", J. Constr. Steel Res., 156, 266-275. https://doi.org/10.1016/j.jcsr.2019.02.011. 
  54. Wen, Y. (2004), "Probabilistic aspects of earthquake engineering", Earthquake Engineering from Engineering Seismology to Performance-Based Engineering, CRC Press, Boca Raton, FL, USA. 
  55. Zaker Esteghamati, M., Banazadeh, M. and Huang, Q. (2018), "The effect of design drift limit on the seismic performance of RC dual high-rise buildings", Struct. Des. Tall Spec. Build., 27(8), e1464. https://doi.org/10.1002/tal.1464.