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Selection of Intensity Measure in Probabilistic Seismic Risk Assessment of a Turkish Railway Bridge

Authors: M. F. Yilmaz, B. Ö. Çağlayan


Fragility curve is an effective common used tool to determine the earthquake performance of structural and nonstructural components. Also, it is used to determine the nonlinear behavior of bridges. There are many historical bridges in the Turkish railway network; the earthquake performances of these bridges are needed to be investigated. To derive fragility curve Intensity measures (IMs) and Engineering demand parameters (EDP) are needed to be determined. And the relation between IMs and EDP are needed to be derived. In this study, a typical simply supported steel girder riveted railway bridge is studied. Fragility curves of this bridge are derived by two parameters lognormal distribution. Time history analyses are done for selected 60 real earthquake data to determine the relation between IMs and EDP. Moreover, efficiency, practicality, and sufficiency of three different IMs are discussed. PGA, Sa(0.2s) and Sa(1s), the most common used IMs parameters for fragility curve in the literature, are taken into consideration in terms of efficiency, practicality and sufficiency.

Keywords: railway bridges, earthquake performance, fragility analyses, selection of intensity measures

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[1] A. Çağlıyan and A. B. Yıldız, “Türkiye’de Demiryolu Güzergâhlari Jeomorfoloji Ilişkisi (Turkey Association of Railway Routes-Geomorphology),” Marmara Coğrafya Dergisi, pp. 466–486, 2013.
[2] Y. Pan, A. K. Agrawal, and M. Ghosn, “Seismic Fragility of Continuous Steel Highway Bridges in New York State,” J. Bridg. Eng., vol. 12, no. 6, pp. 689–699, Nov. 2007.
[3] M. Shinozuka, M. Q. Feng, H.-K. Kim, and S. Kim, “Nonlinear Static Procedure for Fragility Curve Development,” J. Eng. Mech., vol. 126, no. December, pp. 1287–1295, 2000.
[4] C. A. Cornell, F. Jalayer, R. O. Hamburger, and D. A. Foutch, “Probabilistic Basis for 2000 SAC Federal Emergency Management Agency Steel Moment Frame Guidelines,” J. Struct. Eng., vol. 128, no. 4, pp. 526–533, Apr. 2002.
[5] K. R. Mackie and B. Stojadinovic, “Comparison of Incremental Dynamic, Cloud and Stripe Methods for computing Probabilistic Seismic Demand Models,” in Structural Congress 2005, 2005.
[6] N. Luco and C. A. Cornell, “Structure-Specific Scalar Intensity Measures for Near-Source and Ordinary Earthquake Ground Motions,” Earthq. Spectra, vol. 23, no. 2, pp. 357–392, May 2007.
[7] K. Mackie and B. Stojadinovic, “Seismic Demands for Performance-Based Design of Bridges,” 2003.
[8] J. E. Padgett, B. G. Nielson, and R. DesRoches, “Selection of optimal intensity measures in probabilistic seismic demand models of highway bridge portfolios,” Earthq. Eng. Struct. Dyn., vol. 37, no. 5, pp. 711–725, Apr. 2008.
[9] J. W. Baker, “Efficient analytical fragility function fitting using dynamic structural analysis,” 2015.
[10] N. Shome, “Probabilistic seismic demand analysis of nonlinear structures,” Stanford University, 1999.
[11] D. Vamvatsikos and C. Allin Cornell, “Incremental dynamic analysis,” Earthq. Eng. Struct. Dyn., vol. 31, no. 3, pp. 491–514, 2002.
[12] K. R. Mackie and Nielson B.G., “Uncertainty Quantification in Analytical Bridge Fragility Curves,” TCLEE Lifeline Earthq. Eng. a Multihazard Environ., no. 407, pp. 1–12, 2009.
[13] K. Mackie, J.-M. Wong, and B. Stojadinovic, “Integrated Probabilistic Performance-Based Evaluation of Benchmark Reinforced Concrete Bridges,” 2008.