CVM-S4.26

Chen, Po; Lee, En‐Jui

doi:10.1007/978-3-319-16604-9_6

Cited by 8 publications

(4 citation statements)

References 184 publications

(238 reference statements)

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“… a Values from Willis and Clahan (2006). b Zx indicates the depth to the sediment layer with X km/s shear-wave velocity—values from CVM-S4.26 M01 (Chen and Lee, 2015). …”

Section: Description Of Stochastic Gmm and Physics-based Site Datamentioning

confidence: 99%

“…Each fault considers multiple hypocenters and slip distributions—hypocenters are placed every 20 km along the strike, and two slip distributions are run for each hypocenter. An anelastic wave propagation simulation calculates strain Green’s tensors (SGTs) using the SCEC Community Velocity Model (CVM) 4.26 (Chen and Lee, 2015) using a parallelized anelastic finite-difference algorithm. Seismic reciprocity is applied to post-process SGT and to obtain synthetic seismograms (Graves and Wald, 2001; Zhao et al, 2006).…”

Section: Description Of Stochastic Gmm and Physics-based Site Datamentioning

confidence: 99%

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Site-specific stochastic ground motion model utilizing deterministic physics-informed simulations: A Bayesian approach

Senthil,

Lin

2024

Earthquake Spectra

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Limited availability of recorded ground motions poses a challenge for reliable probabilistic seismic-hazard analysis (PSHA), even in highly seismic regions like the Western United States. Stochastic ground motions are commonly employed to address this challenge. However, the stochastic ground motion models (GMMs) may not consistently generate ground motions compatible with the site hazard due to their calibration using global data, failing to capture site-specific characteristics adequately. In the absence of recorded motions, physics-informed simulations provide a viable alternative but are deterministic with limitations of their own that makes them challenging to support PSHA. This article introduces a Bayesian framework that combines prior knowledge from a stochastic GMM, calibrated with global data, with site-specific data obtained from deterministic physics-informed simulations. The proposed framework utilizes the Rezaeian–Der Kiureghian (2010) model as the stochastic GMM and incorporates site-specific data from the CyberShake 15.12 study. By updating the mean and variance of the predictive relationships, along with the marginal distribution of the model parameters, through Bayesian inference, this framework allows for the simulation of site-specific ground motions consistent with the site characteristics. The statistics of peak ground acceleration distributions, as well as both the median and variability of the elastic response spectra, obtained from the calibrated stochastic GMM, demonstrate consistency with those derived using GMMs based on the Next Generation Attenuation (NGA) database.

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“… a Values from Willis and Clahan (2006). b Zx indicates the depth to the sediment layer with X km/s shear-wave velocity—values from CVM-S4.26 M01 (Chen and Lee, 2015). …”

Section: Description Of Stochastic Gmm and Physics-based Site Datamentioning

confidence: 99%

Section: Description Of Stochastic Gmm and Physics-based Site Datamentioning

confidence: 99%

Site-specific stochastic ground motion model utilizing deterministic physics-informed simulations: A Bayesian approach

Senthil,

Lin

2024

Earthquake Spectra

View full text Add to dashboard Cite

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“…Case study site locations in the Los Angeles basin: (a) topographic map with site locations and fault traces; (b) contour map of Z 2.5 , where Z 2.5 indicates the depth to sediment layer with 2.5 km/s shear-wave velocity, values from velocity model CVM-S4.26 (Chen and Lee 2015). …”

Section: Case Study Sites and Structuresmentioning

confidence: 99%

Quantification of the Influence of Deep Basin Effects on Structural Collapse Using SCEC CyberShake Earthquake Ground Motion Simulations

2019

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This paper examines the effects of earthquake ground motions in deep sedimentary basins on structural collapse risk using physics-based earthquake simulations of the Los Angeles basin developed through the Southern California Earthquake Center's CyberShake project. Distinctive waveform characteristics of deep basin seismograms are used to classify the ground motions into several archetype groups, and the damaging influence of the basin effects are evaluated by comparing nonlinear structural responses under spectrum and significant duration equivalent basin and nonbasin ground motions. The deep basin ground motions are observed to have longer period-dependent durations and larger sustained spectral intensities than nonbasin motions for vibration periods longer than about 1.5 s, which can increase structural collapse risk by up to 20% in ground motions with otherwise comparable peak spectral accelerations and significant durations. Two new metrics are proposed to quantify period-dependent duration effects that are not otherwise captured by conventional ground motion intensity measures. The proposed sustained amplitude response spectra and significant duration spectra show promise for characterizing the damaging effects of long duration features of basin ground motions on buildings and other structures.

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“…Once the ruptures and their variations are defined, CyberShake uses an anelastic wave propagation simulation to calculate strain Green tensors around the site of interest. The Green tensors are calculated via reciprocity within the prescribed 3D velocity structure (obtained from the SCEC CVM4.26, Chen and Lee, 2015) using a parallelized anelastic finite difference algorithm (Graves, 1996; Graves and Wald, 2001). Seismic reciprocity is used to process the Green tensors and obtain synthetic seismograms (e.g.…”

Section: Introductionmentioning

confidence: 99%

Efficient intensity measures and machine learning algorithms for collapse prediction of tall buildings informed by SCEC CyberShake ground motion simulations

2020

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In contrast to approaches based on scaling of recorded seismograms, using extensive inventories of numerically simulated earthquakes avoids the need for any selection and scaling of motions which implicitly requires assumptions on intensity measures (IMs) that correlate with structural response. This study has the objectives to examine seismogram features that control the collapse response of tall buildings and to develop efficient and reliable collapse classification algorithms. To that end, machine learning techniques are applied to the results of nonlinear response history analyses of a 20-story tall building performed using about two million simulated ground motions. Feature selection of ground motion IMs generally confirms current understanding of collapse predictors based on previous studies using scaled recorded motions. In addition, interrogations of the large collection of hazard-consistent simulations demonstrate the utility of different IMs for collapse risk assessment in a way that is not possible with recorded motions. Finally, a small subset of IMs is identified and used in development of an efficient collapse classification algorithm which is tested on benchmark simulated data at several sites in the Los Angeles basin.

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CVM-S4.26

Cited by 8 publications

References 184 publications

Site-specific stochastic ground motion model utilizing deterministic physics-informed simulations: A Bayesian approach

Site-specific stochastic ground motion model utilizing deterministic physics-informed simulations: A Bayesian approach

Quantification of the Influence of Deep Basin Effects on Structural Collapse Using SCEC CyberShake Earthquake Ground Motion Simulations

Efficient intensity measures and machine learning algorithms for collapse prediction of tall buildings informed by SCEC CyberShake ground motion simulations

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