A Computational Tool for Ground-Motion Simulations Incorporating Regional Crustal Conditions

2020 ◽  
Author(s):  
Yuxiang Tang ◽  
Nelson Lam ◽  
Hing-Ho Tsang
Keyword(s):  
Ground Motion ◽  
Strong Motion ◽  
Prediction Equation ◽  
Stochastic Simulations ◽  
Computational Tool ◽  
Conditional Mean Spectrum ◽  
Real Earthquake ◽  
Strong Motion Database ◽  
User Friendly ◽  
Seismological Model

Abstract This article introduces a computational tool, namely ground-motion simulation system (GMSS), for generating synthetic accelerograms based on stochastic simulations. The distinctive feature of GMSS is that it has two independently developed upper-crustal models (expressed in the form of shear-wave velocity profiles), which have been built into the program for deriving the frequency-dependent crustal factors, and one of these models was originally developed by the authors. GMSS also has provisions to allow the user to specify their own preferred crustal profile. Sufficient details of both crustal models (forming part of the seismological model) and the accelerogram simulation methodology are presented herein in one article, to allow any person who has programming skills (on a user-friendly platform such as MATLAB, see Data and Resources), to develop their computational tools to implement any further innovations in crustal modeling for direct engineering applications. Crustal properties deep into bedrock can only be accounted for implicitly by conventional ground-motion prediction equation (GMPE) as much depends on the region where the ground motion was recorded. This limitation of existing GMPEs poses a challenge to engineering in regions that are not well represented by any strong-motion database. Toward the end of this article, readers are enlightened with the potential transdisciplinary utility of using GMSS, to facilitate the retrieval and scaling of accelerograms sourced from a database of real earthquake records through the construction of a conditional mean spectrum.

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 Simulation of non-stationary stochastic ground motions based on recent Italian earthquakes

2021 ◽  
Author(s):  
Fabio Sabetta ◽  
Antonio Pugliese ◽  
Gabriele Fiorentino ◽  
Giovanni Lanzano ◽  
Lucia Luzi
Keyword(s):  
Ground Motion ◽  
Ground Motions ◽  
Strong Motion ◽  
Stochastic Simulations ◽  
Model Parameters ◽  
Motion Model ◽  
Arias Intensity ◽  
Time Frequency ◽  
Ground Motion Model ◽  
Ground Motion Records

AbstractThis work presents an up-to-date model for the simulation of non-stationary ground motions, including several novelties compared to the original study of Sabetta and Pugliese (Bull Seism Soc Am 86:337–352, 1996). The selection of the input motion in the framework of earthquake engineering has become progressively more important with the growing use of nonlinear dynamic analyses. Regardless of the increasing availability of large strong motion databases, ground motion records are not always available for a given earthquake scenario and site condition, requiring the adoption of simulated time series. Among the different techniques for the generation of ground motion records, we focused on the methods based on stochastic simulations, considering the time- frequency decomposition of the seismic ground motion. We updated the non-stationary stochastic model initially developed in Sabetta and Pugliese (Bull Seism Soc Am 86:337–352, 1996) and later modified by Pousse et al. (Bull Seism Soc Am 96:2103–2117, 2006) and Laurendeau et al. (Nonstationary stochastic simulation of strong ground-motion time histories: application to the Japanese database. 15 WCEE Lisbon, 2012). The model is based on the S-transform that implicitly considers both the amplitude and frequency modulation. The four model parameters required for the simulation are: Arias intensity, significant duration, central frequency, and frequency bandwidth. They were obtained from an empirical ground motion model calibrated using the accelerometric records included in the updated Italian strong-motion database ITACA. The simulated accelerograms show a good match with the ground motion model prediction of several amplitude and frequency measures, such as Arias intensity, peak acceleration, peak velocity, Fourier spectra, and response spectra.

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.

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.

Performance of response spectral models against New Zealand data

2017 ◽  
Vol 50 (1) ◽  
pp. 21-38 ◽  
Author(s):  
Chris Van Houtte
Keyword(s):  
New Zealand ◽  
Seismic Hazard ◽  
Ground Motion ◽  
Epistemic Uncertainty ◽  
Hazard Analysis ◽  
Strong Motion ◽  
Seismic Hazard Assessment ◽  
Crustal Earthquakes ◽  
Spectral Models ◽  
Strong Motion Database

An important component of seismic hazard assessment is the prediction of the potential ground motion generated by a given earthquake source. In New Zealand seismic hazard studies, it is commonplace for analysts to only adopt one or two models for predicting the ground motion, which does not capture the epistemic uncertainty associated with the prediction. This study analyses a suite of New Zealand and international models against the New Zealand Strong Motion Database, both for New Zealand crustal earthquakes and earthquakes in the Hikurangi subduction zone. It is found that, in general, the foreign models perform similarly or better with respect to recorded New Zealand data than the models specifically derived for New Zealand application. Justification is given for using global models in future seismic hazard analysis in New Zealand. Although this article does not provide definitive model weights for future hazard analysis, some recommendations and guidance are provided.

A Novel Regression Analysis Method for Randomly Truncated Strong-Motion Data

2019 ◽  
Vol 35 (2) ◽  
pp. 977-1001 ◽  
Author(s):  
Shu-Hsien Chao ◽  
Yi-Hau Chen
Keyword(s):  
Regression Analysis ◽  
Ground Motion ◽  
Strong Motion ◽  
Prediction Equation ◽  
Intensity Measurement ◽  
Likelihood Method ◽  
Biased Sampling ◽  
Motion Data ◽  
Triggering Threshold ◽  
Normally Distributed

Regression analysis is a basic and essential tool for developing the ground motion prediction equation (GMPE). Generally, the probability of intensity measurement for a given ground motion scenario described by several predictors is assumed to be normally distributed. However, because of the triggering threshold of the strong-motion station, ground motion records below the triggering threshold are truncated (i.e., not recorded), and the truncated intensity levels of spectral accelerations at different periods are random variables. Consequently, the sampling of the ground motion data used in GMPE development is biased, and the observed probability of the intensity measurement is no longer normally distributed. Therefore, a novel two-step maximum-likelihood method is proposed in this paper as a regression tool to overcome this problem in GMPE development. The advantage of the proposed method is that the correlation between records from the same events and those from the same sites as well as the biased sampling problem can be considered simultaneously, and more ground motion data can be considered to derive more reliable analysis results.

NGA Project Strong-Motion Database

2008 ◽  
Vol 24 (1) ◽  
pp. 23-44 ◽  
Author(s):  
Brian Chiou ◽  
Robert Darragh ◽  
Nick Gregor ◽  
Walter Silva
Keyword(s):  
Time Series ◽  
Ground Motion ◽  
Model Development ◽  
Strong Motion ◽  
Corner Frequency ◽  
Engineering Practice ◽  
Data Set ◽  
Acceleration Time ◽  
Crustal Earthquakes ◽  
Strong Motion Database

A key component of the NGA research project was the development of a strong-motion database with improved quality and content that could be used for ground-motion research as well as for engineering practice. Development of the NGA database was executed through the Lifelines program of the PEER Center with contributions from several research organizations and many individuals in the engineering and seismological communities. Currently, the data set consists of 3551 publicly available multi-component records from 173 shallow crustal earthquakes, ranging in magnitude from 4.2 to 7.9. Each acceleration time series has been corrected and filtered, and pseudo absolute spectral acceleration at multiple damping levels has been computed for each of the 3 components of the acceleration time series. The lowest limit of usable spectral frequency was determined based on the type of filter and the filter corner frequency. For NGA model development, the two horizontal acceleration components were further rotated to form the orientation-independent measure of horizontal ground motion (GMRotI50). In addition to the ground-motion parameters, a large and comprehensive list of metadata characterizing the recording conditions of each record was also developed. NGA data have been systematically checked and reviewed by experts and NGA developers.

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