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.

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.

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.

Site characterisation of GeoNet stations for the New Zealand Strong Motion Database

2017 ◽  
Vol 50 (1) ◽  
pp. 39-49 ◽  
Author(s):  
Anna Kaiser ◽  
Chris Van Houtte ◽  
Nick Perrin ◽  
Liam Wotherspoon ◽  
Graeme McVerry
Keyword(s):  
New Zealand ◽  
Shear Wave ◽  
Ground Motion ◽  
Shear Wave Velocity ◽  
Strong Motion ◽  
Motion Prediction ◽  
Ground Motion Prediction ◽  
High Quality ◽  
Site Specific ◽  
Strong Motion Database

The New Zealand Strong Motion Database provides a wealth of new strong motion data for engineering applications. In order to utilise these data in ground motion prediction, characterisation of key site parameters at each of the ~497 past and present GeoNet strong motion stations represented in the database is required. Here, we present the compilation of a complete set of site metadata for the New Zealand database, including four key parameters: i) NZS1170.5 site subsoil classification, ii) the time-averaged shear-wave velocity to a depth of 30 m (Vs30), iii) fundamental site period (Tsite) and iv) depth to a shear-wave velocity of 1000 m/s (Z1.0, a proxy for depth to bedrock). In addition, we have assigned a quality estimate (Quality 1 – 3) to each numerical parameter to provide a qualitative estimate of the uncertainty. New high-quality Tsite, Vs30 and Z1.0 estimates have been obtained from a variety of recent studies, and reconciled with available geological information. This database will be used in efforts to guide development and testing of new and existing ground motion prediction models in New Zealand, allowing re-examination of the most important site parameters that control site response in a New Zealand setting. Preliminary analyses, using the newly compiled data, suggest that high quality site parameters can reduce uncertainty in ground motion prediction. Furthermore, the database can be used to identify suitable rock reference sites for seismological research, and as a guide to more detailed site-specific references in the literature. The database provides an additional resource for informing engineering design, however it is not suitable as a replacement for site-specific assessment.

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