Probabilistic seismic response and uncertainty analysis of continuous bridges under near-fault ground motions

Abstract

Performance-based seismic design can generate predictable structure damage result with given seismic hazard. However, there are multiple sources of uncertainties in the seismic design process that can affect desired performance predictability. This paper mainly focuses on the effects of near-fault pulse-like ground motions and the uncertainties in bridge modeling on the seismic demands of regular continuous highway bridges. By modeling a regular continuous bridge with OpenSees software, a series of nonlinear dynamic time-history analysis of the bridge at three different site conditions under near-fault pulse-like ground motions are carried out. The relationships between different Intensity Measure (IM) parameters and the Engineering Demand Parameter (EDP) are discussed. After selecting the peak ground acceleration as the most correlated IM parameter and the drift ratio of the bridge column as the EDP parameter, a probabilistic seismic demand model is developed for near-fault earthquake ground motions for 3 different site conditions. On this basis, the uncertainty analysis is conducted with the key sources of uncertainty during the finite element modeling. All the results are quantified by the “swing” base on the specific distribution range of each uncertainty parameter both in near-fault and far-fault cases. All the ground motions are selected from PEER database, while the bridge case study is a typical regular highway bridge designed in accordance with the Chinese Guidelines for Seismic Design of Highway Bridges. The results show that PGA is a proper IM parameter for setting up a linear probabilistic seismic demand model; damping ratio, pier diameter and concrete strength are the main uncertainty parameters during bridge modeling, which should be considered both in near-fault and far-fault ground motion cases.

Keywords

continuous bridge / probabilistic seismic demand model / Intensity Measure / near-fault / uncertainty

参考文献

[1] SEAOC. Vision 2000: Performance Based Seismic Engineering of Buildings. Sacramento, CA: Structural Engineers Association of California, 1995

[2] Fan L C. Life cycle and performance based seismic design of major bridges in China. Frontiers of Architecture and Civil Engineering in China, 2007, 1(3): 261–266

[3] Ghobarah A. Performance-based design in earthquake engineering: State of development. Engineering Structures, 2001, 23(8): 878–884

[4] Deierlein G G, Krawinkler H, Cornell C A. A framework for performance-based earthquake engineering. In: Proceedings of 2003 Pacific Conference on Earthquake Engineering. Christchurch: New Zealand Society for Earthquake Engineering, 2003

[5] Porter K A. An overview of PEER’s performance-based earthquake engineering methodology. In: Proceedings of the 9th International Conference on Applications of Statistics and Probability in Civil Engineering. San Francisco: Civil Engineering Risk and Reliability Association, 2003

[6] Moehle J, Deierlein G G. A framework methodology for performance-based engineering. In: Proceedings of the 13th World Conference on Earthquake Engineering. Vancouver B C: Canadian Association for Earthquake Engineering, 2004: 1–13

[7] Porter K A, Beck J L, Shaikhutdinov R V. Sensitivity of building loss estimates to major uncertain variables. Earthquake Spectra, 2002, 18(4): 719–743

[8] Vu-Bac N, Silani M, Lahmer T, Zhuang X, Rabczuk T. A unified framework for stochastic predictions of mechanical properties of polymeric nanocomposites. Computational Materials Science, 2015, 96: 520–535

[9] Vu-Bac N, Lahmer T, Keitel H, Zhao J, Zhuang X, Rabczuk T. Stochastic predictions of bulk properties of amorphous polyethylene based on molecular dynamics simulations. Mechanics of Materials, 2014, 68: 70–84

[10] Vu-Bac N, Lahmer T, Zhuang X, Nguyen-Thoi T, Rabczuk T. A software framework for probabilistic sensitivity analysis for computationally expensive models. Advances in Engineering Software, 2016, 100: 19–31

[11] Pan Y, Agrawal A K, Ghosn M. Seismic fragility of continuous steel highway bridges in New York state. Journal of Bridge Engineering, 2007, 12(6): 689–699

[12] Tubaldi E, Barbato M, Dall’asta A. Influence of model parameter uncertainty on seismic transverse response and vulnerability of steel-concrete composite bridges with dual load path. Journal of Structural Engineering, 2012, 138(3): 363–374

[13] Padgett J E, DesRoches R. Sensitivity of seismic response and fragility to parameter uncertainty. Journal of Structural Engineering, 2007, 133(12): 1710–1718

[14] Wang Z, Padgett J E, Dueñas-Osorio L. Toward a uniform risk design philosophy: Quantification of uncertainties for highway bridge portfolios. In: Proceeding of the 7th National Seismic Conference on Bridges & Highways. Oakland: Multidisciplinary Center for Earthquake Engineering Research, 2013

[15] Somerville P G, Smith N F, Graves R W, Abrahamson N A. Modification of empirical strong ground attenuation relations to include the amplitude and duration effects of rupture directivity. Seismological Research Letters, 1997, 68(1): 199–222

[16] Niazi M, Bozorgnia Y. Behaviour of near-source peak vertical and horizontal ground motions over SMART-1 array, Taiwan. Bulletin of the Seismological Society of America, 1991, 81(3): 715–732

[17] Chopra A K, Chintanapakdee C. Comparing response of SDOF systems to near-fault and far-fault Earthquake motions in the context of spectral regions. Earthquake Engineering & Structural Dynamics, 2001, 30(12): 1769–1789

[18] Porter K A, Beck J L, Shaikhutdinov R V. Sensitivity of building loss estimates to major uncertain variables. Earthquake Spectra, 2002, 18(4): 719–743

[19] Ministry of Communications of the People’s Republic of China. Guidelines for Seismic Design of Highway Bridges (JTG/T B02–01–2008). Beijing: People’s Communications Press, 2008 (in Chinese)

[20] Mazzoni S, McKenna F, Scott M, Fenves G. Open System for Earthquake Engineering Simulation (OpenSees), User Command Language Manual. Berkeley: Pacific Earthquake Engineering Research Center, University of California, 2006

[21] Hambly E C. Bridge Deck Behavior. 2nd ed. New York: Van Nostrand Reinhold, 1991

[22] Hamdia K M, Silani M, Zhuang X, He P, Rabczuk T. Stochastic analysis of the fracture toughness of polymeric nanoparticle composites using polynomial chaos expansions. International Journal of Fracture, 2017, 206(2): 215–227

[23] Hamdia K M, Ghasemi H, Zhuang X, Alajlan N, Rabczuk T. Sensitivity and uncertainty analysis for flexoelectric nanostructures. Computer Methods in Applied Mechanics and Engineering, 2018, 337: 95–109

[24] Vu-Bac N, Lahmer T, Zhang Y, Zhuang X, Rabczuk T. Stochastic predictions of interfacial characteristic of polymeric nanocomposites (PNCs). Composites. Part B, Engineering, 2014, 59: 80–95

[25] Vu-Bac N, Rafiee R, Zhuang X, Lahmer T, Rabczuk T. Uncertainty quantification for multiscale modelling of polymer nanocomposites with correlated parameters. Composites. Part B, Engineering, 2015, 68: 446–464

[26] Nielson B, Desroches R. Analytical seismic fragility curves for typical bridges in the central and south-eastern United States. Earthquake Spectra, 2007, 23(3): 615–633

[27] Yu X H. Probabilistic seismic fragility and risk analysis of reinforced concrete frame structures. Dissertation for the Doctoral Degree. Harbin: Harbin Institute of Technology, 2012 (in Chinese)

[28] The Ministry of Communications of the People’s Republic of China. General Code for Design of Highway Bridges and Culverts, JTG D60. China Beijing: Communications Press, 2004 (in Chinese)

[29] Wu W P. Seismic fragility of reinforced concrete bridges with consideration of various sources of uncertainty. Dissertation for the Doctoral Degree. Changsha: Hunan University, 2016 (in Chinese).

[30] Padgett J E, Nielson B G, Desroches R. Selection of optimal intensity measures in probabilistic seismic demand models of highway bridge portfolios. Earthquake Engineering & Structural Dynamics, 2008, 37(5): 711–725

[31] National Standard of the People’s Republic of China. GB 50010–2010. Code for Design of Concrete Structures. Beijing: China Architecture and Building Press, 2010 (in Chinese)

[32] Pang Y T, Wu X, Shen G Y, Yuan W C. Seismic fragility analysis of cable-stayed bridges considering different sources of uncertainties. Journal of Bridge Engineering, 2014, 19(4): 04013015

[33] Ma H B, Zhuo W D, Yin G, Sun Y, Chen L B. A probabilistic seismic demand model for regular highway bridges. Applied Mechanics and Materials, 2016, 847: 307–318

[34] Lv H S, Zhao F X. Site coefficients suitable to China site category. Earth Science, 2007, 29(1): 67–76

[35] Chiou B, Darragh R, Gregor N, Silva W. NGA project strong-motion database. Earthquake Spectra, 2008, 24(1): 23–44

[36] Chinese Ministry of Housing and Urban Rural Development. Code for Seismic Design of Urban Bridges, CJJ 166–2011. Beijing: China Architecture and Building Press, 2011 (in Chinese)

[37] Morris M D. Factorial sampling plans for preliminary computational experiments. Technometrics, 1991, 33(2): 161–174

[38] Porter K A, Beck J L, Shaikhutdinov R V. Sensitivity of building loss estimates to major uncertain variables. Earthquake Spectra, 2002, 18(4): 719–743

Acknowledgment

This work was supported by the Poliba2China Project Funding (Italy Code Number: CUPD96D17000110002), and the National Natural Science Foundation of China (Grant No. 51878180), and the Transportation Science and Technology Development Project of Fujian Province (No. 201803).

版权

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