Estimating Unknown Input Parameters when Implementing the NGA Ground-Motion Prediction Equations in Engineering Practice

Kaklamanos, James; Baise, Laurie G.; Boore, David M.

doi:10.1193/1.3650372

Cited by 160 publications

(58 citation statements)

References 27 publications

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“…For the target IMs, GMPEs used in the Western US are considered here, 9,46-48 whereas the suggestions by Kaklamanos et al 49 were adopted to estimate unknown inputs for some of the GMPEs. As target, IM predictions from individual GMPEs as well as the average of their predictions will be adopted later.…”

Section: Illustrative Implementationmentioning

confidence: 99%

Modification of stochastic ground motion models for matching target intensity measures

Tsioulou

Taflanidis

Galasso

2017

Earthq Engng Struct Dyn

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SummaryStochastic ground motion models produce synthetic time-histories by modulating a white noise sequence through functions that address spectral and temporal properties of the excitation. The resultant ground motions can be then used in simulation-based seismic risk assessment applications. This is established by relating the parameters of the aforementioned functions to earthquake and site characteristics through predictive relationships. An important concern related to the use of these models is the fact that through current approaches in selecting these predictive relationships, compatibility to the seismic hazard is not guaranteed. This work offers a computationally efficient framework for the modification of stochastic ground motion models to match target intensity measures (IMs) for a specific site and structure of interest. This is set as an optimization problem with a dual objective. The first objective minimizes the discrepancy between the target IMs and the predictions established through the stochastic ground motion model for a chosen earthquake scenario. The second objective constraints the deviation from the model characteristics suggested by existing predictive relationships, guaranteeing that the resultant ground motions not only match the target IMs but are also compatible with regional trends. A framework leveraging kriging surrogate modeling is formulated for performing the resultant multi-objective optimization, and different computational aspects related to this optimization are discussed in detail. The illustrative implementation shows that the proposed framework can provide ground motions with high compatibility to target IMs with small only deviation from existing predictive relationships and discusses approaches for selecting a final compromise between these two competing objectives. | INTRODUCTIONThe growing interest in performance-based earthquake engineering 1,2 and in simulation-based, seismic risk assessment approaches 3,4 has increased in the past decades the relevance of ground motion modeling techniques. These techniques describe the entire time-series of seismic excitations, providing a characterization appropriate for dynamic time-history analysis. UndoubtedlyThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Illustrative Implementationmentioning

confidence: 99%

Modification of stochastic ground motion models for matching target intensity measures

Tsioulou

Taflanidis

Galasso

2017

Earthq Engng Struct Dyn

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“…Given the synthetic fault parameters and a randomly chosen hypocenter position on the fault, three other types of distances commonly used in GMPEs are computed: the Joyner-Boore distance (R JB ; closest horizontal distance to the surface projection of the fault plane), the rupture distance (R rup ; closest distance to the fault plane), and the hypocentral distance (R hyp ). Kaklamanos et al (2010) developed a computation scheme to compute R rup from R JB . We slightly modified the Kaklamanos et al (2010) relationships to compute (from geometrical considerations) all of the distance types based on the epicentral distance and on the source-to-site azimuth.…”

Section: Simulations and Gmpes Simulation Settingsmentioning

confidence: 99%

“…Kaklamanos et al (2010) developed a computation scheme to compute R rup from R JB . We slightly modified the Kaklamanos et al (2010) relationships to compute (from geometrical considerations) all of the distance types based on the epicentral distance and on the source-to-site azimuth. The distances metrics are compared in Figure 8, which shows that, as expected, R epi is always greater than or equal to R JB , and R hypo is always greater than or equal to R rup .…”

Section: Simulations and Gmpes Simulation Settingsmentioning

confidence: 99%

Regional Stochastic GMPEs in Low‐Seismicity Areas: Scaling and Aleatory Variability Analysis—Application to the French Alps

Drouet¹,

Cotton²

2015

Bulletin of the Seismological Society of America

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We generate stochastic ground-motion prediction equations (GMPEs) for a wide magnitude range (M w 3-8) that are adapted to the French Alps. Based on inversions of source, path, and site terms from weak-motion accelerometric data (Drouet et al., 2010), we build seismological stochastic models to use in conjunction with the simulation program Stochastic-Method SIMulation (SMSIM) to stochastically simulate ground-motion response spectral amplitudes. All the input parameters are considered random variables, and the uncertainty is propagated through simulations by random sampling of the corresponding distributions. Constant and magnitude-dependent stress parameter models are compared with variable large-magnitude stress levels. Stochastic simulations are performed for periods between 0.01 and 3 s, M w from 3 to 8, epicentral distances from 1 to 250 km, and two site conditions: rock (V S30 800 m=s) and hard rock (V S30 2000 m=s). These synthetic data are then regressed to produce stochastic GMPEs using an up-to-date regression form, the parameterization of which can be defined in terms of different distance metrics (i.e., epicentral, hypocentral, Joyner-Boore, or rupture distance). The impact of the uncertainty on each input parameter on the GMPE standard deviation is determined through sensitivity analysis. The major contributors to the uncertainty are the site model, both V S30 and κ (the high-frequency parameter), which affect the within-event standard deviation, and the uncertainty on the stress parameter, which affects the between-event term. The GMPEs are compared to real data using statistical analysis of residuals. Two sets of strong-motion data are considered (the Reference Database for Seismic Ground Motion in Europe [RESORCE] and the worldwide Next Generation Attenuation databases), as well as weak-motion data recorded in France. The results show that the magnitude-dependent stress parameter models (for magnitudes below 4.6) fit the French data better, and a large-magnitude constant stress parameter of 10 MPa gives a better fit to strong-motion data.

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“…In the selection process, we decided not to down-weight GMPEs with difficult-toimplement parameters (e.g., basin depth terms or depth to top of rupture), because those issues can be overcome with appropriate parameter selection protocols (e.g., Kaklamanos et al, 2011). We also decided not to down-weight GMPEs that either lack site terms or whose modeling of site response is non-optimal (e.g., lack of nonlinearity) because GMPEs can be evaluated for a reference rock site condition in hazard analysis and site effects subsequently added in a hybrid process (Cramer, 2003;Goulet and Stewart, 2009).…”

Section: Selection Procedures and Factors Consideredmentioning

confidence: 99%

Selection of Ground Motion Prediction Equations for the Global Earthquake Model

et al. 2015

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Ground-motion prediction equations (GMPEs) relate ground-motion intensity measures to variables describing earthquake source, path, and site effects. We select from many available GMPEs those models recommended for use in seismic hazard assessments in the Global Earthquake Model. We present a GMPE selection procedure that evaluates multi-dimensional ground motion trends (e.g., with respect to magnitude, distance and structural period), examines functional forms, and evaluates published quantitative tests of GMPE performance against independent data. Our recommendations include: four models, based principally on simulations, for stable continental regions (SCRs); three empirical models for interface and in-slab subduction zone (SZ) events; and three empirical models for active shallow crustal regions (ACRs). To approximately incorporate epistemic uncertainties, the selection process accounts for alternate representations of key GMPE attributes, such as the rate of distance attenuation, which are defensible from available data. Recommended models for each domain will change over time as additional GMPEs are developed.

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Estimating Unknown Input Parameters when Implementing the NGA Ground-Motion Prediction Equations in Engineering Practice

Cited by 160 publications

References 27 publications

Modification of stochastic ground motion models for matching target intensity measures

Modification of stochastic ground motion models for matching target intensity measures

Regional Stochastic GMPEs in Low‐Seismicity Areas: Scaling and Aleatory Variability Analysis—Application to the French Alps

Selection of Ground Motion Prediction Equations for the Global Earthquake Model

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