Alternative Hybrid Empirical Ground‐Motion Model for Central and Eastern North America Using Hybrid Simulations and NGA‐West2 Models

2016 ◽  
Vol 106 (2) ◽  
pp. 734-754 ◽  
Author(s):  
Alireza Shahjouei ◽  
Shahram Pezeshk
2019 ◽  
Vol 35 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Georgios Zalachoris ◽  
Ellen M. Rathje

A ground motion model (GMM) tuned to the characteristics of the observed, and potentially induced, seismicity in Texas, Oklahoma, and Kansas is developed using a database of 4,528 ground motions recorded during 376 events of Mw > 3.0 in the region. The GMM is derived using the referenced empirical approach with an existing Central and Eastern North America model as the reference GMM and is applicable for Mw = 3.0–5.8 and hypocentral distances less than 500 km. The proposed model incorporates weaker magnitude scaling than the reference GMM for periods less than about 1.0 s, resulting in smaller predicted ground motions at larger magnitudes. The proposed model predicts larger response spectral accelerations at short hypocentral distances (≤20 km), which is likely because of the shallow hypocenters of events in Texas, Oklahoma, and Kansas. Finally, the VS30 scaling for the newly developed model predicts less amplification at VS30 < 600 m/s than the reference GMM, which is likely because of the generally thinner sediments in the study area. This finding is consistent with recent studies regarding site amplification in Central and Eastern North America.


2019 ◽  
Vol 109 (2) ◽  
pp. 732-744 ◽  
Author(s):  
Z. Farajpour ◽  
S. Pezeshk ◽  
M. Zare

1995 ◽  
Vol 85 (1) ◽  
pp. 17-30 ◽  
Author(s):  
Gail M. Atkinson ◽  
David M. Boore

Abstract Predictive relations are developed for ground motions from eastern North American earthquakes of 4.0 ≦ M ≦ 7.25 at distances of 10 ≦ R ≦ 500 km. The predicted parameters are response spectra at frequencies of 0.5 to 20 Hz, and peak ground acceleration and velocity. The predictions are derived from an empirically based stochastic ground-motion model. The relations differ from previous work in the improved empirical definition of input parameters and empirical validation of results. The relations are in demonstrable agreement with ground motions from earthquakes of M 4 to 5. There are insufficient data to adequately judge the relations at larger magnitudes, although they are consistent with data from the Saguenay (M 5.8) and Nahanni (M 6.8) earthquakes. The underlying model parameters are constrained by empirical data for events as large as M 6.8.


2014 ◽  
Vol 6 (2) ◽  
pp. 141-161 ◽  
Author(s):  
Radu Vacareanu ◽  
Sorin Demetriu ◽  
Dan Lungu ◽  
Florin Pavel ◽  
Cristian Arion ◽  
...  

Author(s):  
Rocco di Filippo ◽  
Giuseppe Abbiati ◽  
Osman Sayginer ◽  
Patrick Covi ◽  
Oreste S. Bursi ◽  
...  

Abstract Seismic risk evaluation of coupled systems of industrial plants often needs the implementation of complex finite element models to consider their multicomponent nature. These models typically rely on significant computational resources. Moreover, the relationships between seismic action, system response and relevant damage levels are often characterized by a high level of nonlinearity, thus requiring a solid background of experimental data. Furthermore, fragility analyses depend on the adoption of a significant number of seismic waveforms generally not available when the analysis is site-specific. To propose a methodology able to manage these issues, we present a possible approach for a seismic reliability analysis of a coupled tank-piping system. The novelty of this approach lies in the adoption of artificial accelerograms, FE models and experimental hybrid simulations to evaluate a surrogate meta-model of our system. First, to obtain the necessary input for a stochastic ground motion model able to generate synthetic ground motions, a disaggregation analysis of the seismic hazard is performed. Hereafter, we reduce the space of parameters of the stochastic ground motion model by means of a global sensitivity analysis upon the seismic response of our system. Hence, we generate a large set of synthetic ground motions and select, among them, a few signals for experimental hybrid simulations. In detail, the hybrid simulator is composed by a numerical substructure to predict the sliding response of a steel tank, and a physical substructure made of a realistic piping network. Furthermore, we use these experimental results to calibrate a refined ANSYS FEM. More precisely, we focus on tensile hoop strains in elbow pipes as a leading cause for leakage, monitoring them with strain gauges. Thus, we present the procedure to evaluate a numerical Kriging meta-model of the coupled system based on both experimental and finite element model results. This model will be adopted in a future development to carry out a seismic fragility analysis.


2021 ◽  
pp. 875529302110348
Author(s):  
Emily M Gibson ◽  
Michelle T Bensi

Risk analysis and risk-informed design of spatially distributed infrastructure systems for earthquake hazards require an understanding of and ability to model the spatial correlation of ground motion prediction errors. We assess this spatial correlation in Central and Eastern North America (CENA) by calculating ground motion residuals and semivariograms from earthquake recordings in the Next Generation Attenuation (NGA)-East database. Although data limitations prohibit the development of a reliable model to capture this correlation, we have made notable findings relevant to future risk analyses. The spatial correlation of ground motion prediction errors in CENA is larger than those previously published for shallow crustal regions, which agrees with the lower attenuation observed in CENA. Differences in correlation behavior is also observed between tectonic and induced event recordings. This is, in part, due to the characteristics of the system of stations in which they were recorded. In addition, the choice of ground motion model (GMM) used to calculate the predicted ground motions was found to have an impact on the resulting correlation of errors and we recommend that future CENA spatial correlation models be tailored to the specific infrastructure system and location that will be analyzed.


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