A Comparison of Ground Motion Prediction Equations for Arias Intensity and Cumulative Absolute Velocity Developed Using a Consistent Database and Functional Form

2012 ◽  
Vol 28 (3) ◽  
pp. 931-941 ◽  
Author(s):  
Kenneth W. Campbell ◽  
Yousef Bozorgnia
Keyword(s):  
Ground Motion ◽  
Functional Form ◽  
Strong Motion ◽  
Prediction Equation ◽  
Absolute Velocity ◽  
Motion Prediction ◽  
Ground Motion Prediction ◽  
Arias Intensity ◽  
Intensity Measures ◽  
Cumulative Absolute Velocity

Arias intensity (AI) and cumulative absolute velocity (CAV) have been proposed as instrumental intensity measures that can incorporate the cumulative effects of ground motion duration and intensity on the response of structural and geotechnical systems. In this study, we have developed a ground motion prediction equation (GMPE) for the horizontal component of AI in order to compare its predictability to a similar GMPE for CAV. Both GMPEs were developed using the same strong motion database and functional form in order to eliminate any bias these factors might cause in the comparison. This comparison shows that AI exhibits significantly greater amplitude scaling and aleatory uncertainty than CAV. The smaller standard deviation and less sensitivity to amplitude suggests that CAV is more predictable than AI and should be considered as an alternative to AI in engineering and geotechnical applications where the latter intensity measure is traditionally used.

A Ground Motion Prediction Equation for the Horizontal Component of Cumulative Absolute Velocity (CAV) Based on the PEER-NGA Strong Motion Database

2010 ◽  
Vol 26 (3) ◽  
pp. 635-650 ◽  
Author(s):  
Kenneth W. Campbell ◽  
Yousef Bozorgnia
Keyword(s):  
Ground Motion ◽  
Strong Motion ◽  
Prediction Equation ◽  
Horizontal Component ◽  
Motion Prediction ◽  
Prediction Equations ◽  
Ground Motion Prediction ◽  
Intensity Measure ◽  
Cumulative Absolute Velocity ◽  
Strong Motion Database

Cumulative absolute velocity (CAV), defined as the integral of the absolute acceleration time series, has been used as an index to indicate the possible onset of structural damage to nuclear power plant facilities and liquefaction of saturated soils. However, there are very few available ground motion prediction equations for this intensity measure. In this study, we developed a new empirical prediction equation for the horizontal component of CAV using the strong motion database and functional forms that were used to develop similar prediction equations for peak response parameters as part of the PEER Next Generation Attenuation (NGA) Project. We consider this relationship to be valid for magnitudes ranging from 5.0 up to 7.5–8.5 (depending on fault mechanism) and distances ranging from 0–200 km. We found the interevent, intra-event, and intracomponent standard deviations from this relationship to be smaller than any intensity measure we have investigated thus far.

scholarly journals Ground motion models for Arias intensity, cumulative absolute velocity, peak incremental ground velocity, and significant duration in New Zealand

2019 ◽  
Vol 52 (4) ◽  
pp. 193-207 ◽  
Author(s):  
Zach Bullock
Keyword(s):  
New Zealand ◽  
Ground Motion ◽  
Absolute Velocity ◽  
Taupo Volcanic Zone ◽  
Arias Intensity ◽  
Intensity Measures ◽  
Motion Models ◽  
Cumulative Absolute Velocity ◽  
Functional Forms ◽  
Ground Motion Models

This study proposes empirical ground motion models for a variety of non-spectral intensity measures and significant durations in New Zealand. Equations are presented for the prediction of the median and maximum rotated components of Arias intensity, cumulative absolute velocity, cumulative absolute velocity above a 5 cm/s2 acceleration threshold, peak incremental ground velocity, and the 5% to 75% and 5% to 95% significant durations. Recent research has highlighted the usefulness of these parameters in both structural and geotechnical engineering. The New Zealand Strong Motion Database provides the database for regression and includes many earthquakes from all regions of New Zealand with the exceptions of Auckland and Northland, Otago and Southland, and Taranaki. The functional forms for the proposed models are selected using cross validation. The possible influence of effects not typically included in ground motion models for these intensity measures is considered, such as hanging wall effects and basin depth effects, as well as altered attenuation in the Taupo Volcanic Zone. The selected functional forms include magnitude and rupture depth scaling, attenuation with distance, and shallow site effects. Finally, the spatial autocorrelation of the models’ within-event residuals is considered and recommendations are made for developing correlated maps of intensity predictions stochastically.

Implementing horizontal-to-vertical Fourier spectral ratios and spatial correlation in a ground-motion prediction equation to predict site effects

2020 ◽  
pp. 875529302095244
Author(s):  
Shu-Hsien Chao ◽  
Che-Min Lin ◽  
Chun-Hsiang Kuo ◽  
Jyun-Yan Huang ◽  
Kuo-Liang Wen ◽  
...  
Keyword(s):  
Spatial Correlation ◽  
Ground Motion ◽  
Prediction Accuracy ◽  
Site Effects ◽  
Strong Motion ◽  
Prediction Equation ◽  
Motion Prediction ◽  
Ground Motion Prediction Equation ◽  
Ground Motion Prediction ◽  
Spectral Ratios

We propose a methodology to implement horizontal-to-vertical Fourier spectral ratios (HVRs) evaluated from strong ground motion induced by earthquake (EHVRs) or ambient ground motion observed from microtremor (MHVRs) individually and simultaneously with the spatial correlation (SC) in a ground-motion prediction equation (GMPE) to improve the prediction accuracy of site effects. We illustrated the methodology by developing an EHVRs-SC-based model which supplements Vs30 and Z1.0 with the SC and EHVRs collected at strong motion stations, and a MHVRs-SC-based model that supplements Vs30 and Z1.0 with the SC and MHVRs observed from microtremors at sites which were collocated with strong motion stations. The standard deviation of the station-specific residuals can be reduced by up to 90% when the proposed models are used to predict site effects. In the proposed models, the spatial distribution of the predicted station terms for peak ground acceleration (PGA) from MHVRs at 3699 sites is consistent with that of the predicted station terms for PGA from EHVRs at 721 strong motion stations. Prediction accuracy for stations with inferred Vs30 is similar to that of stations with measured Vs30 with the proposed models. This study provides a methodology to simultaneously implement SC and EHVRs, or SC and MHVRs in a GMPE to improve the prediction accuracy of site effects for a target site with available EHVRs or MHVRs information.

scholarly journals Correlation between Ground Motion and Building Response using California Earthquake Records

2015 ◽  
Vol 31 (4) ◽  
pp. 2027-2046 ◽  
Author(s):  
Matthieu Perrault ◽  
Philippe Guéguen
Keyword(s):  
Ground Motion ◽  
Functional Form ◽  
Construction Material ◽  
Strong Motion ◽  
Prediction Equation ◽  
Intensity Measures ◽  
Damage Prediction ◽  
Using Data ◽  
The Relationship ◽  
Damage Criteria

Using data from the California Strong Motion Instrumentation Program, we studied the relationship between building response and parameters describing the noxiousness of ground motion. According to vulnerability methods that use structural drift as damage criteria, we estimated the building response on the basis of the normalized relative roof displacement (NRRD), considered as damage criteria. The relationships between the NRRD and the intensity measures of the ground motion are developed using simulated annealing method. Grouping buildings by typology (defined according to their main construction material and height) reduces the variability of the building response. Furthermore, by combining IMs, the NRRD can be predicted more accurately by a building damage prediction equation. A functional form is thus proposed to estimate the NRRD for several building typologies, calibrated on the building responses recorded in California. This functional form can be used to obtain a fast and overall damage forecast after an earthquake.

Ground motion prediction equations for Arias Intensity using the Kik-net database

2020 ◽  
pp. 875529302093881
Author(s):  
Mahdi Bahrampouri ◽  
Adrian Rodriguez-Marek ◽  
Russell A Green
Keyword(s):  
Ground Motion ◽  
Slope Failure ◽  
Energy Content ◽  
Motion Prediction ◽  
Ground Motion Prediction Equations ◽  
Prediction Equations ◽  
Ground Motion Prediction ◽  
Arias Intensity ◽  
Intensity Measures ◽  
Earthquake Scenario

In seismic design, intensity measures are selected on the basis of how well these parameters correlate with the damage caused by earthquakes and on our ability to predict these intensity measures for a given earthquake scenario. As an index for the energy content of ground motions, Arias Intensity has proved to be efficient in several applications, including the prediction of earthquake-induced slope failure and damage in structures, and to a lesser extent liquefaction triggering. In this article, the Kiban-Kyoshin network (KiK)-net database is used to present ground motion prediction equations (GMPEs) for Arias Intensity of shallow crustal and subduction zone earthquakes. The proposed GMPEs are applicable for M 4-9. The predictive models incorporate the average shear-wave velocity over the upper 30 meters (VS30) for the prediction of site effects. The proposed relationships include additional attenuation for paths that cross the volcanic fronts and different attenuation for forearc and backarc regions of Japan.

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

2019 ◽  
Vol 35 (3) ◽  
pp. 1289-1310 ◽  
Author(s):  
Kenneth W. Campbell ◽  
Yousef Bozorgnia
Keyword(s):  
Ground Motion ◽  
Earthquake Engineering ◽  
Goodness Of Fit ◽  
Performance Metrics ◽  
Absolute Velocity ◽  
Arias Intensity ◽  
Intensity Measures ◽  
Motion Models ◽  
Cumulative Absolute Velocity ◽  
Ground Motion Models

We updated our Next Generation Attenuation (NGA)-West1 ground motion models (GMMs) for the horizontal components of Arias intensity (AI) and cumulative absolute velocity (CAV) using the functional form and NGA-West2 database we used to develop GMMs for peak-amplitude and peak-spectral ground motion intensity measures (GMIMs). Our results show that CAV has the best goodness-of-fit statistics of all the GMIMs we have evaluated up to this time. Its relatively small between- and within-event standard deviations confirm its superior predictability. On the other hand, AI has the highest standard deviation of any GMIM we have studied thus far, which is approximately double that of CAV. Although either CAV or AI or a combination of both have been shown to meet various performance metrics proposed in the context of performance-based earthquake engineering (PBEE), CAV's high level of predictability makes it superior to AI for use in engineering applications, such as PBEE, that involve probabilistic inference.

Ground motion prediction equation for crustal earthquakes in Taiwan

2020 ◽  
Vol 36 (4) ◽  
pp. 2129-2164
Author(s):  
Van-Bang Phung ◽  
Chin Hsiung Loh ◽  
Shu Hsien Chao ◽  
Brian SJ Chiou ◽  
Bor-Shouh Huang
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Functional Form ◽  
Hazard Analysis ◽  
Prediction Equation ◽  
Motion Prediction ◽  
Ground Motion Prediction Equation ◽  
Ground Motion Prediction ◽  
Crustal Earthquakes ◽  
Magnitude Scaling

We develop a ground motion prediction equation (GMPE) for estimating horizontal ground motion amplitudes caused by crustal earthquakes, based on an integrated data set that includes strong motion recordings mainly from Taiwan earthquakes and only from large magnitude earthquakes in the NGA-West2 database. This GMPE is developed for probabilistic seismic hazard analysis study, which is introduced as a part of Taiwan Senior Seismic Hazard Analysis Committee Level 3 projects. The functional form developed by Chiou and Youngs was carefully studied to determine the key modeling parameters needed to regress against ground motion in the target region. Using this functional form, the GMPE achieves considerable improvement over previously developed Taiwan GMPEs. In particular, the use of a high-order function in magnitude scaling enables representation of the saturation effects of large earthquakes. Moreover, consideration of focal mechanisms, depth effects, and dip effects are used to correct the magnitude scaling; consideration of nonlinear site amplification is conditioned on VS30 and reference ground motion on rock; and consideration of basin depth effect is a function of Z1.0 in correlation with VS30. In addition, ground motion data used in this study are not only expanded by more than three times as many earthquakes and records compared with a previous Taiwan model but also provide the metadata of these records that were not available or were previously incomplete. In this study, we compare the proposed model with the NGA-West2 models and discuss the regional difference in ground motion in terms of spectral shape, magnitude scaling, distance scaling, depth scaling, style of faulting, and site effects. We provide median and single standard deviations of peak ground acceleration and 5% damped pseudospectral acceleration response ordinates of the orientation-independent average horizontal component of ground motion (RotD50) for the spectral period of 0.01–10 s.

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