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

doi:10.1007/s11709-019-0577-8

Front. Struct. Civ. Eng.

2019, Vol. 13

Issue (6) : 1510-1519 https://doi.org/10.1007/s11709-019-0577-8

RESEARCH ARTICLE

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

Hai-Bin MA¹, Wei-Dong ZHUO¹(

), Davide LAVORATO², Camillo NUTI^1,², Gabriele FIORENTINO², Giuseppe Carlo MARANO^1,³, Rita GRECO³, Bruno BRISEGHELLA¹

¹. College of Civil Engineering, Fuzhou University, Fuzhou 350108, China
². Department of Architecture, Roma Tre University, Roma 00154, Italy
³. College of Civil Engineering and Architecture, Politecnico di Bari University, Bari 70126, Italy

Download: PDF(1386 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

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

Corresponding Author(s): Wei-Dong ZHUO

Just Accepted Date: 04 September 2019 Online First Date: 31 October 2019 Issue Date: 21 November 2019

Cite this article:

Hai-Bin MA,Wei-Dong ZHUO,Davide LAVORATO, et al. Probabilistic seismic response and uncertainty analysis of continuous bridges under near-fault ground motions[J]. Front. Struct. Civ. Eng., 2019, 13(6): 1510-1519.

URL:

https://academic.hep.com.cn/fsce/EN/10.1007/s11709-019-0577-8
https://academic.hep.com.cn/fsce/EN/Y2019/V13/I6/1510

Tab.1 Definition of regular bridges

Fig.1 Fiber element of the column.

Fig.2 3-dimensional FE model of the bridge.

Fig.3 Analytical model of regular highway bridge indicating probabilistic models of uncertain parameters.

Tab.2 Modeling related parameters and the distribution value

Fig.4 Selected records for the probabilistic seismic analysis (Site I-15 records; Site II-35 records; Site III-15 records).

Fig.5 Relationship between the DRand different IM parameter on the base of the results of the nonlinear analyses on bridge sample on site II condition: (a) PGA; (b) PGV; (c) PGD; (d) PGV/PGA; (e) S_a; (f) predominat period.

Fig.6 Distribution of DRand PGAin natural logarithmic coordinate system on 3 different site condition: (a) Site I-15 records; (b) Site II-35 records; (c) Site III-15 records.

Tab.3 Parameters of the selected seismic design level

Fig.7 Selected records for the damage mechanism comparison: (a) near-fault records; (b) far-fault records (Site I-15 records; Site II-15 records; Site III-15 records).

Fig.8 Tornado diagrams for the example bridge on site condition I (NF-Near-fault, FF-Fault fault). (a) NF-m; (b) NF-s; (c) FF-m; (d) FF-s.

Fig.9 Tornado diagrams for the example bridge on site condition II (NF-Near-fault, FF-Fault fault). (a) NF-m; (b) NF-s; (c) FF-m; (d) FF-s.

Fig.10 Tornado diagrams for the example bridge on site condition III (NF-Near-fault, FF-Fault fault). (a) NF-m; (b) NF-s; (c) FF-m; (d) FF-s.

Fig.11 Tornado diagrams for the example bridge on site condition I. (a) NF-m; (b) NF-s; (c) FF-m; (d) FF-s.

Fig.12 Tornado diagrams for the example bridge on site condition II. (a) NF-m; (b) NF-s; (c) FF-m; (d) FF-s.

Fig.13 Tornado diagrams for the example bridge on site condition III. (a) NF-m; (b) NF-s; (c) FF-m; (d) FF-s.

1	SEAOC. Vision 2000: Performance Based Seismic Engineering of Buildings. Sacramento, CA: Structural Engineers Association of California, 1995
2	L C Fan. 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 https://doi.org/10.1007/s11709-007-0033-z
3	A Ghobarah. Performance-based design in earthquake engineering: State of development. Engineering Structures, 2001, 23(8): 878–884 https://doi.org/10.1016/S0141-0296(01)00036-0
4	G G Deierlein, H Krawinkler, C A Cornell. 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	K A Porter. 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	J Moehle, G G Deierlein. 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	K A Porter, J L Beck, R V Shaikhutdinov. Sensitivity of building loss estimates to major uncertain variables. Earthquake Spectra, 2002, 18(4): 719–743 https://doi.org/10.1193/1.1516201
8	N Vu-Bac, M Silani, T Lahmer, X Zhuang, T Rabczuk. A unified framework for stochastic predictions of mechanical properties of polymeric nanocomposites. Computational Materials Science, 2015, 96: 520–535 https://doi.org/10.1016/j.commatsci.2014.04.066
9	N Vu-Bac, T Lahmer, H Keitel, J Zhao, X Zhuang, T Rabczuk. Stochastic predictions of bulk properties of amorphous polyethylene based on molecular dynamics simulations. Mechanics of Materials, 2014, 68: 70–84 https://doi.org/10.1016/j.mechmat.2013.07.021
10	N Vu-Bac, T Lahmer, X Zhuang, T Nguyen-Thoi, T Rabczuk. A software framework for probabilistic sensitivity analysis for computationally expensive models. Advances in Engineering Software, 2016, 100: 19–31 https://doi.org/10.1016/j.advengsoft.2016.06.005
11	Y Pan, A K Agrawal, M Ghosn. Seismic fragility of continuous steel highway bridges in New York state. Journal of Bridge Engineering, 2007, 12(6): 689–699 https://doi.org/10.1061/(ASCE)1084-0702(2007)12:6(689)
12	E Tubaldi, M Barbato, A Dall’asta. 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 https://doi.org/10.1061/(ASCE)ST.1943-541X.0000456
13	J E Padgett, R DesRoches. Sensitivity of seismic response and fragility to parameter uncertainty. Journal of Structural Engineering, 2007, 133(12): 1710–1718 https://doi.org/10.1061/(ASCE)0733-9445(2007)133:12(1710)
14	Z Wang, J E Padgett, L Dueñas-Osorio. 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	P G Somerville, N F Smith, R W Graves, N A Abrahamson. 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 https://doi.org/10.1785/gssrl.68.1.199
16	M Niazi, Y Bozorgnia. 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	A K Chopra, C Chintanapakdee. 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 https://doi.org/10.1002/eqe.92
18	K A Porter, J L Beck, R V Shaikhutdinov. Sensitivity of building loss estimates to major uncertain variables. Earthquake Spectra, 2002, 18(4): 719–743 https://doi.org/10.1193/1.1516201
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	S Mazzoni, F McKenna, M Scott, G Fenves. Open System for Earthquake Engineering Simulation (OpenSees), User Command Language Manual. Berkeley: Pacific Earthquake Engineering Research Center, University of California, 2006
21	E C Hambly. Bridge Deck Behavior. 2nd ed. New York: Van Nostrand Reinhold, 1991
22	K M Hamdia, M Silani, X Zhuang, P He, T Rabczuk. Stochastic analysis of the fracture toughness of polymeric nanoparticle composites using polynomial chaos expansions. International Journal of Fracture, 2017, 206(2): 215–227 https://doi.org/10.1007/s10704-017-0210-6
23	K M Hamdia, H Ghasemi, X Zhuang, N Alajlan, T Rabczuk. Sensitivity and uncertainty analysis for flexoelectric nanostructures. Computer Methods in Applied Mechanics and Engineering, 2018, 337: 95–109 https://doi.org/10.1016/j.cma.2018.03.016
24	N Vu-Bac, T Lahmer, Y Zhang, X Zhuang, T Rabczuk. Stochastic predictions of interfacial characteristic of polymeric nanocomposites (PNCs). Composites. Part B, Engineering, 2014, 59: 80–95 https://doi.org/10.1016/j.compositesb.2013.11.014
25	N Vu-Bac, R Rafiee, X Zhuang, T Lahmer, T Rabczuk. Uncertainty quantification for multiscale modelling of polymer nanocomposites with correlated parameters. Composites. Part B, Engineering, 2015, 68: 446–464 https://doi.org/10.1016/j.compositesb.2014.09.008
26	B Nielson, R Desroches. Analytical seismic fragility curves for typical bridges in the central and south-eastern United States. Earthquake Spectra, 2007, 23(3): 615–633 https://doi.org/10.1193/1.2756815
27	X H Yu. 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	W P Wu. 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	J E Padgett, B G Nielson, R Desroches. Selection of optimal intensity measures in probabilistic seismic demand models of highway bridge portfolios. Earthquake Engineering & Structural Dynamics, 2008, 37(5): 711–725 https://doi.org/10.1002/eqe.782
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	Y T Pang, X Wu, G Y Shen, W C Yuan. Seismic fragility analysis of cable-stayed bridges considering different sources of uncertainties. Journal of Bridge Engineering, 2014, 19(4): 04013015 https://doi.org/10.1061/(ASCE)BE.1943-5592.0000565
33	H B Ma, W D Zhuo, G Yin, Y Sun, L B Chen. A probabilistic seismic demand model for regular highway bridges. Applied Mechanics and Materials, 2016, 847: 307–318 https://doi.org/10.4028/www.scientific.net/AMM.847.307
34	H S Lv, F X Zhao. Site coefficients suitable to China site category. Earth Science, 2007, 29(1): 67–76
35	B Chiou, R Darragh, N Gregor, W Silva. NGA project strong-motion database. Earthquake Spectra, 2008, 24(1): 23–44 https://doi.org/10.1193/1.2894831
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	M D Morris. Factorial sampling plans for preliminary computational experiments. Technometrics, 1991, 33(2): 161–174 https://doi.org/10.1080/00401706.1991.10484804
38	K A Porter, J L Beck, R V Shaikhutdinov. Sensitivity of building loss estimates to major uncertain variables. Earthquake Spectra, 2002, 18(4): 719–743 https://doi.org/10.1193/1.1516201

[1]	Hanli WU, Hua ZHAO, Jenny LIU, Zhentao HU. A filtering-based bridge weigh-in-motion system on a continuous multi-girder bridge considering the influence lines of different lanes[J]. Front. Struct. Civ. Eng., 2020, 14(5): 1232-1246.
[2]	Wei XIONG, Shan-Jun ZHANG, Li-Zhong JIANG, Yao-Zhuang LI. Parametric study on the Multangular-Pyramid Concave Friction System (MPCFS) for seismic isolation[J]. Front. Struct. Civ. Eng., 2020, 14(5): 1152-1165.
[3]	Farhoud KALATEH, Farideh HOSSEINEJAD. Uncertainty assessment in hydro-mechanical-coupled analysis of saturated porous medium applying fuzzy finite element method[J]. Front. Struct. Civ. Eng., 2020, 14(2): 387-410.
[4]	Fengjie TAN, Tom LAHMER. Shape design of arch dams under load uncertainties with robust optimization[J]. Front. Struct. Civ. Eng., 2019, 13(4): 852-862.
[5]	Jiping GE, M. Saiid SAIIDI, Sebastian VARELA. Computational studies on the seismic response of the State Route 99 bridge in Seattle with SMA/ECC plastic hinges[J]. Front. Struct. Civ. Eng., 2019, 13(1): 149-164.
[6]	Arash SEKHAVATIAN, Asskar Janalizadeh CHOOBBASTI. Application of random set method in a deep excavation: based on a case study in Tehran cemented alluvium[J]. Front. Struct. Civ. Eng., 2019, 13(1): 66-80.
[7]	Ahmad IDRIS, Indra Sati Hamonangan HARAHAP, Montasir Osman Ahmed ALI. Jack up reliability analysis: An overview[J]. Front. Struct. Civ. Eng., 2018, 12(4): 504-514.
[8]	Chu MAI, Katerina KONAKLI, Bruno SUDRET. Seismic fragility curves for structures using non-parametric representations[J]. Front. Struct. Civ. Eng., 2017, 11(2): 169-186.
[9]	Prishati RAYCHOWDHURY,Sumit JINDAL. Shallow foundation response variability due to soil and model parameter uncertainty[J]. Front. Struct. Civ. Eng., 2014, 8(3): 237-251.
[10]	Gang WANG. Efficiency of scalar and vector intensity measures for seismic slope displacements[J]. Front Struc Civil Eng, 2012, 6(1): 44-52.

Viewed

Full text

Abstract

Cited

Shared

Discussed