ABSTRACT
Seismic intensity measures (IMs) provide a link between the seismic hazard and the dynamic response of structures subjected to earthquake shaking. The spectral acceleration at the first and usually dominant vibration mode, Sa(T1), is a popular choice for building structures. Meanwhile, the IM selection for bridges is non-trivial since they do not typically possess a single dominant mode. Even for ordinary bridges with a dominant mode, the behavior can change significantly in each principal direction through the activation, or yielding, of its different components. This study examines the performance of a novel IM that incorporates ground motion directionality and structure non-linearity in this context: the nnth percentile of all rotation angles of the inelastic spectral displacement, Sdi,RotDnn. This evaluation is carried out within the context of an ordinary bridge structure and is compared with other conventional IMs used in regional risk assessment of bridges. The case study bridge utilized is a highway overcrossing located in California with two spans and a continuous prestressed reinforced concrete box girder deck section. A large ground motion set was selected from the NGA-West2 database, and incremental dynamic analysis was carried out on the structure to assess the IM performance to characterize collapse. The results indicate that Sdi,RotDnn performs very well compared to other IMs for the bridge structure and could be a prudent choice to characterize inelastic response of bridges with several possible mechanisms in different principal directions. Also, using the RotD50 definition, typically used in ground motion models, showed a 17.3% increase in efficiency compared to RotD100 definition typically used in engineering practice.
Nomenclature
AvgSa | = | average spectral acceleration |
EDP | = | engineering demand parameter |
fc | = | cut-off frequency of second-order Butterworth low-pass filter |
FIV3 | = | filtered incremental velocity |
fn | = | natural frequency |
GMM | = | ground motion model |
IDA | = | incremental dynamic analysis |
IM | = | intensity measure |
IVs | = | incremental velocities |
Mw | = | moment magnitude |
N | = | Number of spectral accelerations used for the calculation of average spectral acceleration |
PBEE | = | performance-based earthquake engineering |
PGA | = | peak ground acceleration |
PGD | = | peak ground displacement |
PGV | = | peak ground velocity |
PSHA | = | probabilistic seismic hazard assessment |
R | = | strength ratio, also known as force reduction factor |
R2 | = | coefficient of determination |
RotDnn | = | nnth fractile of a response spectral value for all rotation angles sorted by amplitude |
Rrup | = | rupture distance |
rup | = | ground motion rupture parameter |
ŝ | = | logarithmic mean of collapse intensities from all the ground motions |
Sa(T) | = | spectral acceleration at period T |
Sdi,RotDnn | = | nnth percentile of all rotation angles of the inelastic spectral displacement |
SDOF | = | single-degree-of-freedom |
SF | = | scaling factor |
si | = | collapse intensity of the ith ground motion |
SRS | = | simplified relative sufficiency |
T | = | oscillation period |
tend | = | last instant of time of the acceleration time series |
U | = | translational modal participation factor |
ügf | = | filtered acceleration time series |
Vs(t) | = | series of incremental velocities |
Vs,30 | = | time-averaged shear-wave velocity for the top 30 m of soil |
Vs,max | = | local maximum IVs in Vs(t) |
Vs,min | = | local minimum IVs in Vs(t) |
α·Tn | = | time segment duration for the calculation of IV |
β | = | scalar that controls the fc/fn ratio |
β0,s | = | y-intercept of the log-linear interpolation |
β1,s | = | slope of the log-linear interpolation |
βEDP|IM | = | dispersion of EDP given IM |
βIM|rup | = | Dispersion of IM given a set of rupture parameters |
βRTR | = | record-to-record variability |
εs | = | collapse intensity residuals |
εsu | = | steel ultimate strain |
κ | = | SaRotD100/SaRotD50 |
κi | = | Sdi,RotD100/Sdi,RotD50 |
Φ | = | Rotational modal participation factor |
Acknowledgments
The work presented in this paper has been developed within the framework of the project “Dipartimenti di Eccellenza”, funded by the Italian Ministry of Education, University and Research at IUSS Pavia. The comments and feedback of Davit Shahnazaryan are also gratefully acknowledged in addition to the discussions with Karim Tarbali during the early development of this research.
Disclosure Statement
No potential conflict of interest was reported by the author(s).
Data Availability Statement
The data and models used as part of this study will be made available upon request.