Probabilistic seismic demand analysis of steel moment frames by utilising Bayesian statistics

References

• Baker, J. W., & Cornell A. C. (2005). A vector‐valued ground motion intensity measure consisting of spectral acceleration and epsilon. Earthquake Engineering & Structural Dynamics, 34(10), 1193–1217. DOI: 10.1002/eqe.474.
   Web of Science ®Google Scholar
• Baker, J. W., & Cornell, C. A. (2006). Correlation of response spectral values for multicomponent ground motions. Bulletin of the Seismological Society of America, 96(1), 215–227. DOI: 10.1785/0120050060.
   Web of Science ®Google Scholar
• Belitz, C., Brezger, A., Klein, N., Kneib, T., Lang, S., & Umlauf, N. (2015). Software for Bayesian inference in Structural Additive Regression Models (Version 3.0.2.). Wilhelmsplatz, Germany: University of Göttingen.
   Google Scholar
• Bensi, M. T. (2010). A Bayesian network methodology for infrastructure seismic risk assessment and decision support CA. Berkeley: University of California.
   Google Scholar
• Bozorgnia, Y., & Bertero, V. V. (2004). Earthquake engineering: from engineering seismology to performance-based engineering. Boca Raton, Florida, USA: CRC press.
   Google Scholar
• Cornell, C. A., Jalayer, F., Hamburger, R. O., & Foutch, D. A. (2002). Probabilistic basis for 2000 SAC federal emergency management agency steel moment frame guidelines. Journal of Structural Engineering, 128(4), 526–533. DOI: 10.1061/(ASCE)0733-9445(2002)128:4(526).
   Web of Science ®Google Scholar
• Der Kiureghian, A. (1999). A Bayesian framework for fragility assessment. ICASP 8 Conf, Sydney, Australia.
   Google Scholar
• Esmaili, O., Grant Ludwig, L., & Zareian, F. (2016). Improved performance‐based seismic assessment of buildings by utilizing Bayesian statistics. Earthquake Engineering & Structural Dynamics, 45(4), 581–597. DOI: 10.1002/eqe.2672.
   Web of Science ®Google Scholar
• FEMA (1997). NEHRP guidelines for the seismic rehabilitation of buildings. Federal Emergency Management Agency, Washington, DC, USA.
   Google Scholar
• Gardoni, P., Der Kiureghian, A., & Mosalam, K. M. (2002). Probabilistic models and fragility estimates for bridge components and systems. Berkeley, CA: Pacific Earthquake Engineering Research Center.
   Google Scholar
• Ghasemi, H., Zare, M., Fukushima, Y., & Koketsu, K. (2009). An empirical spectral ground-motion model for Iran. Journal of Seismology, 13(4), 499–515. DOI: 10.1007/s10950-008-9143-x.
   Web of Science ®Google Scholar
• Han, S., & Wen, Y. (1997). Method of reliability-based seismic design. I: Equivalent nonlinear systems. Journal of Structural Engineering, 123(3), 256–263. DOI: 10.1061/(ASCE)0733-9445(1997)123:3(256).
   Web of Science ®Google Scholar
• Hariri-Ardebili, M., & Saouma, V. (2016). Probabilistic seismic demand model and optimal intensity measure for concrete dams. Structural Safety, 59, 67–85. DOI: 10.1016/j.strusafe.2015.12.001.
   Web of Science ®Google Scholar
• Ibarra, L. F., Medina, R. A., & Krawinkler, H. (2005). Hysteretic models that incorporate strength and stiffness deterioration. Earthquake Engineering & Structural Dynamics, 34(12), 1489–1511. DOI: 10.1002/eqe.495.
   Web of Science ®Google Scholar
• Iran Strong Motion Network Data Bank. (2016, September). Retrieved from http://www.bhrc.ac.ir
   Google Scholar
• Jalayer, F., & Cornell, C. A. (2003). A technical framework for probability-based demand and capacity factor (DCFD) seismic formats. Berkeley, CA: Pacific Earthquake Engineering Research Center.
   Google Scholar
• Jeon, J. S., Mangalathu, S., Song, J., & DesRoches, R. (2017). Parameterized seismic fragility curves for curved multi-frame concrete box-girder bridges using Bayesian parameter estimation. Journal of Earthquake Engineering. Online publication. DOI: 10.1080/13632469.2017.1342291.
   Web of Science ®Google Scholar
• Kostinakis, K., Fontara, I.-K., & Athanatopoulou, A. M. (2018). Scalar structure-specific ground motion intensity measures for assessing the seismic performance of structures: a review. Journal of Earthquake Engineering, 22(4), 630–665. DOI:10.1080/13632469.2016.1264323.
   Web of Science ®Google Scholar
• Luco, N., & Cornell, C. A. (2007). Structure-specific scalar intensity measures for near-source and ordinary earthquake ground motions. Earthquake Spectra, 23(2), 357–392. DOI: 10.1193/1.2723158.
   Web of Science ®Google Scholar
• Luco, N., Mai, P., Cornell, C., & Beroza, G. (2002). Probabilistic seismic demand analysis, SMRF connection fractures, and near-source effects. Paper presented at the Seventh U.S. National Conference on Earthquake Engineering, Boston, MA.
   Google Scholar
• Mackie, K., & Stojadinović, B. (2001). Probabilistic seismic demand model for California highway bridges. Journal of Bridge Engineering, 6(6), 468–481. DOI: 10.1061/(ASCE)1084-0702(2001)6:6(468).
   Google Scholar
• Mahdavi Adeli, M., Deylami, A., Banazadeh, M., & Alinia, M. M. (2011). A Bayesian approach to construction of probabilistic seismic demand models for steel moment-resisting frames. Scientia Iranica, 18(4), 885–894. DOI: 10.1016/j.scient.2011.07.019.
   Web of Science ®Google Scholar
• Mangalathu, S., Jeon, J.-S., DesRoches, R., & Padgett, J. (2016). Application of Bayesian methods to probabilistic seismic demand analyses of concrete box-girder bridges. Geotechnical and Structural Engineering Congress 2016, Phoenix, AZ.
   Google Scholar
• Medina, R. A., & Krawinkler, H. (2003). Seismic demands for nondeteriorating frame structures and their dependence on ground motions. Berkeley, CA: Pacific Earthquake Engineering Research Center.
   Google Scholar
• Pan, J., Xu, Y., & Jin, F. (2015). Seismic performance assessment of arch dams using incremental nonlinear dynamic analysis. European Journal of Environmental and Civil Engineering, 19(3), 305–326. DOI: 10.1080/19648189.2014.960950.
   Web of Science ®Google Scholar
• PEER ground motion database. (2016, September). Retrieved from http://ngawest2.berkeley.edu/
   Google Scholar
• Pejovic, J. R., Serdar, N. N., & Pejovic, R. R. (2017). Optimal intensity measures for probabilistic seismic demand models of RC high-rise buildings. Earthquakes and Structures, 13(3), 221–231. DOI: 10.12989/eas.2017.13.3.221.
   Web of Science ®Google Scholar
• Rudas, T. (2008). Handbook of probability: theory and applications. Los Angeles, CA, USA: Sage Publications.
   Google Scholar
• Schellenberg, A., Yang, T. T. Y., & Kohama, E. (2016). Open System for Earthquake Engineering Simulation (Version 2.5.7). Berkeley, CA: Pacific Earthquake Engineering Research Center.
   Google Scholar
• Shome, N., Cornell, C. A., Bazzurro, P., & Carballo, J. E. (1998). Earthquakes, records, and nonlinear responses. Earthquake Spectra, 14(3), 469–500. DOI: 10.1193/1.1586011.
   Google Scholar
• Tothong, P., & Cornell, C. A. (2007). Probabilistic seismic demand analysis using advanced ground motion intensity measures, attenuation relationships, and near-fault effects. Berkeley, CA: Pacific Earthquake Engineering Research Center.
   Google Scholar
• Tothong, P., & Luco, N. (2007). Probabilistic seismic demand analysis using advanced ground motion intensity measures. Earthquake Engineering & Structural Dynamics, 36(13), 1837–1860. DOI: 10.1002/eqe.696.
   Web of Science ®Google Scholar
• Yahyaabadi, A., & Nodehi, F. (2015). Probabilistic seismic hazard analysis of Bojnord region by considering near-fault effects. Paper presented at the The 6th International Conference on Earthquake & Structures, Kerman, Iran.
   Google Scholar
• Yahyaabadi, A., & Tehranizadeh, M. (2011). New scalar intensity measure for near-fault ground motions based on the optimal combination of spectral responses. Scientia Iranica, 18(6), 1149–1158. DOI: 10.1016/j.scient.2011.09.013.
   Web of Science ®Google Scholar
• Yahyaabadi, A., & Tehranizadeh, M. (2012). Development of an improved intensity measure in order to reduce the variability in seismic demands under near-fault ground motions. Journal of Earthquake and Tsunami, 06(02), 1250012. DOI: 10.1142/S1793431112500121.
   Google Scholar