Ground Motion Models for the Horizontal Components of Arias Intensity (AI) and Cumulative Absolute Velocity (CAV) Using the NGA-West2 Database

Campbell, Kenneth W.; Bozorgnia, Yousef

doi:10.1193/090818eqs212m

Cited by 52 publications

(58 citation statements)

References 95 publications

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“…This section compares the nine CAV models presented here with existing global models from Campbell and Bozorgnia (2019). We choose to make this comparison because Campbell and Bozorgnia (2019) is the only model in the previous literature with global applicability and a database of more than 3,000 records. That study included comparisons with many of the models in the literature, but because we present nine models whose disagreement is a key feature of the study, we simplify these comparisons to maintain the clarity of the figures.…”

Section: Resultsmentioning

confidence: 99%

“…Three functional forms for the path term, three functional forms for the source term, and one functional form for the site term are used. The NGA-West2 database (Ancheta et al, 2014) provides the basis for the Campbell and Bozorgnia (2019) model and the models proposed here, which are globally applicable for shallow crustal earthquakes. This study fits models to the NGA-West2 data and thus relies heavily on the work done by the NGA-West2 authors in preparing and processing both the acceleration records and the associated metadata.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A suite of ground motion prediction equations for cumulative absolute velocity in shallow crustal earthquakes including epistemic uncertainty

2020

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Recent research has highlighted the usefulness of cumulative absolute velocity [Formula: see text] in several contexts, including using the [Formula: see text] at the ground surface for earthquake early warning and using the [Formula: see text] at rock reference conditions for evaluation of the liquefaction risk facing structures. However, there are relatively few ground motion prediction equations for CAV, they are based on relatively small data sets, and they give relatively similar results. This study develops nine ground motion prediction equations for [Formula: see text] based on a global database of ground motion records from shallow crustal earthquakes. Its provision of nine models enables characterization of epistemic uncertainty for ranges of earthquake characteristics that are sparsely populated in the regression database. The functional forms provide different perspectives on extrapolation to important ranges of earthquake characteristics, particularly large magnitude events and short distances. The variability and epistemic uncertainty in the models are characterized. Spatial autocorrelation of the models’ errors is investigated. The models’ predictions agree with existing broadly applicable models at small to moderate magnitudes and moderate to long distances. These models can be used to improve hazard analysis of [Formula: see text] that incorporates the influence of epistemic uncertainty.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A suite of ground motion prediction equations for cumulative absolute velocity in shallow crustal earthquakes including epistemic uncertainty

2020

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“…Similarly, the spectra shown in Figure 19 and

I_{a_{g e o m}}

C A V_{g e o m}

, and

D_{5 - 95_{g e o m}}

shown in Figure 20B belong to the same scenario. The median predictions of the GGMPM ( i.e RNN predictions) for

I_{a_{g e o m}}

and

C A V_{g e o m}

is further compared against the median predictions of CB19 11 and

D_{5 - 95_{g e o m}}

is compared to AS16 10 . As can be observed from Figure 20, even while maintaining the correlations between the IMs, the RNN framework tends to estimate median values of

I_{a_{g e o m}}

C A V_{g e o m}

, and

D_{5 - 95_{g e o m}}

much closer to the recorded IMs as compared to the CB19 and AS16 GMPEs.…”

Section: Comparison Against Other Gmpesmentioning

confidence: 93%

“…The growing interest of using intensity‐based IMs (such as I a , CAV) (also termed as integral IMs), and duration‐based IMs (such as D 5‐95 ) alongside spectrum‐based IM (such as S a ) as described by Kiani and Pezeshk 7 , Du and Wang 8 , and Yeow et al 9 , has led to the development of various GMPEs. Afshari and Stewart 10 developed a GMPE for geomean of significant duration ( D 5‐95 ), while Campbell and Bozorgnia 11 recently proposed GMPEs for geomean Arias intensity ( I a ) and CAV. Other investigators have developed relationships correlating the amplitude‐ and spectrum‐based IMs (e.g., 12–15 ) and ground motion duration measures 16 .…”

Section: Introductionmentioning

confidence: 99%

Generalized ground motion prediction model using hybrid recurrent neural network

Fayaz

Xiang

Zareian

2020

Earthq Engng Struct Dyn

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Modern seismic hazard analysis utilizes various ground motion prediction equations (GMPEs) to estimate intensity measures (IMs) such as spectral acceleration (Sa), Arias intensity (Ia), significant duration (D5‐95), and cumulative absolute velocity (CAV) as scalar parameters. The use of these GMPEs leads to independent estimations of IMs for the same earthquake scenario. Since the IMs belonging to the same earthquake scenario are naturally correlated, the GMPEs used for characterization of seismic hazard are expected to incorporate such correlation structure. Particularly, Sa corresponding to different periods of a single‐degree‐of‐freedom oscillator is estimated by GMPEs that do not explicitly account for internal correlations between the Sa at various periods for the same ground motion. The current approach to incorporate such a correlation for Sa spectrum is through a post‐processing technique developed by Baker and Jayaram; their method relates the spectral accelerations of different periods using functional forms describing linear correlations. However, the proposed correlation functions can be further improved and extended to more accurately estimate the spectrum of near‐fault high‐magnitude ground motions using high order dependencies among the spectral accelerations. This study proposes a generalized ground motion prediction model (GGMPM) using a hybrid recurrent neural network (RNN) framework that can be used to estimate a 29 × 1 correlated vector (denoted as IM) of RotD50 Sa at 26 periods and geometric means of Ia, D5‐95, and CAV using a set of seismic source and site parameters as inputs. This is an improvement to the current body of knowledge because the high order dependencies between the individual components of IM are incorporated. A RNN framework is developed that estimates the median vector of IM. The discrepancy between the IM estimated using the RNN framework and the IM computed from recorded motions is further minimized using the covariance matrix adaptation evolution strategy (CMA‐ES), which is a non‐convex optimization method. The residuals of the RNN framework are used to construct the inter‐event and the intra‐event covariance matrices to account for the inter‐event and intra‐event variabilities of the ground motions. Hence, given the source and site parameters, the RNN‐framework returns a median prediction of the IM, which is then combined with estimated inter‐event and intra‐event covariance matrices to obtain the probabilistic estimation of IM. Furthermore, this GGMPM framework is compared against various currently used GMPEs, and the results of these comparisons demonstrate that the proposed GGMPM leads to improved predictions while maintaining the internal dependencies of the IM components.

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“…We have just used a limited number of scalar IM s, and vector‐valued IM s are not considered herein (see 37,38 for the estimation of RSM using vector‐valued IM s). It is worth mentioning that all the modifications herein related to the RSM formulation (considering also collapse‐cases, see Equations and , and the use of DM instead of EDP ) are applicable also to vector‐valued IM s. It is worth noting that the selected IM s are hazard‐computable; more specifically, the first five IM s are functions of PGA or Sa at different periods for which the corresponding ground‐motion prediction equations (GMPEs) exists; CAV and AI (as a proxy for energy‐based non‐spectral IM s 55 ) have their own GMPEs 56 . To perform IDA, it is notable that all the IM s herein are monotonic scalable intensity measures (see 51 ).…”

Section: Numerical Applicationmentioning

confidence: 99%

Selection of seismic intensity measures for prescribed limit states using alternative nonlinear dynamic analysis methods

Ebrahimian

Jalayer

2020

Earthq Engng Struct Dyn

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In performance‐based earthquake engineering, the suitability of an intensity measure (IM) is expressed through its efficiency and sufficiency. An efficient IM leads to a small record‐to‐record variability in the estimation of demand given seismic intensity. A sufficient IM is one that renders the estimation of demand for all intensity levels independent of all other ground motion parameters. Given that establishing sufficiency is not a trivial task, the relative sufficiency measure (RSM) has been proposed previously based on information theory concepts. RSM can be employed for quantifying the relative sufficiency of an IM with respect to another IM by the amount of extra information that it relays on average about the ground motion for the estimation of a demand parameter of interest. RSM has been so far conditioned on a linear logarithmic regression probability model, better known as the Cloud Analysis (CA), which relies on unscaled ground‐motion records. This work lays out the methodology for estimating both the efficiency and RSM in terms of the damage measure directly (instead of demand parameter) and by employing alternative nonlinear dynamic analysis procedures (NDAPs), such as, a modified version of CA that considers the collapse‐cases explicitly and the incremental dynamic analysis. It is demonstrated that the RSM can be sensitive to the NDAP employed, while it does not demonstrate significant sensitivity to the limit state of interest. An alternative measure of efficiency (known in literature as proficiency), directly measured as the dispersion in the fragility curve, shows more sensitivity to the limit state.

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Ground Motion Models for the Horizontal Components of Arias Intensity (AI) and Cumulative Absolute Velocity (CAV) Using the NGA-West2 Database

Cited by 52 publications

References 95 publications

A suite of ground motion prediction equations for cumulative absolute velocity in shallow crustal earthquakes including epistemic uncertainty

A suite of ground motion prediction equations for cumulative absolute velocity in shallow crustal earthquakes including epistemic uncertainty

Generalized ground motion prediction model using hybrid recurrent neural network

Selection of seismic intensity measures for prescribed limit states using alternative nonlinear dynamic analysis methods

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