scholarly journals Spectral Representation-Based Multidimensional Nonstationary Ground Motion Model for Seismic Reliability Analysis of Frame Structures

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Lawali Moussa Laminou ◽  
Xinghua Chen
Keyword(s):  
Power Spectrum ◽  
Reliability Analysis ◽  
Ground Motion ◽  
Spectral Representation ◽  
Response Spectrum ◽  
Frame Structure ◽  
Motion Model ◽  
Seismic Responses ◽  
Seismic Reliability ◽  
Ground Motion Model

A framework for a multidimensional nonstationary ground motion model based on spectral representation theory is proposed in this paper. The multidimensional nonstationary ground motion model is built from a local target to fit the multidimensional response spectrum. A four-stage modulation function takes into account the multidimensional intensity correlation and the modified Clough–Penzien (C-P) power spectrum with parameter correlation, which represent the two main aspects, the modulation function and the power spectrum of constructing the multidimensional nonstationary ground motion model. A multidimensional response spectrum constructed according to the standardizing response spectrum is used as the fitting target response spectrum. Samples of random ground motion for random seismic response and dynamic reliability study are finally obtained. The random seismic responses are then combined with the probability density evolution method (PDEM) to carry out the seismic reliability analysis of a randomly base-excited moment-resisting frame structure. In the numerical analysis, the nonlinear seismic responses and reliability of a 10-story reinforced concrete frame structure are carefully investigated in accordance with the Egyptian seismic code. As a result, the effectiveness of the proposed method is fully demonstrated.

A horizontal ground-motion model for crustal and subduction earthquakes in Taiwan

2020 ◽  
Vol 36 (2) ◽  
pp. 463-506 ◽  
Author(s):  
Shu-Hsien Chao ◽  
Brian Chiou ◽  
Chiao-Chu Hsu ◽  
Po-Shen Lin
Keyword(s):  
Ground Motion ◽  
Hazard Analysis ◽  
Response Spectrum ◽  
Likelihood Method ◽  
Biased Sampling ◽  
Motion Model ◽  
Subduction Earthquakes ◽  
Ground Motion Model ◽  
Single Station ◽  
Horizontal Ground Motion

In this study, a new horizontal ground-motion model is developed for crustal and subduction earthquakes in Taiwan. A novel two-step maximum-likelihood method is used as a regression tool to develop this model. This method simultaneously considers both the correlation between records and the biased sampling because of random truncation. Moreover, additional ground-motion data can be considered to derive more reliable analysis results. The functional form of the proposed ground-motion model is constructed using the response spectrum of the reference ground-motion scenario and different scalings of the source, path, and site to illustrate the ground-motion characteristics. The variabilities in the ground-motion intensity that result from different events, stations, and records are developed individually to derive a single-station sigma. The proposed ground-motion model may be useful for predicting ground-motion intensity and performing site-specific probabilistic seismic hazard analysis in Taiwan.

Summary of the BA18 Ground‐Motion Model for Fourier Amplitude Spectra for Crustal Earthquakes in California

2019 ◽  
Vol 109 (5) ◽  
pp. 2088-2105 ◽  
Author(s):  
Jeff Bayless ◽  
Norman A. Abrahamson
Keyword(s):  
Ground Motion ◽  
Earthquake Engineering ◽  
Response Spectrum ◽  
Amplitude Spectrum ◽  
Motion Model ◽  
Fourier Amplitude ◽  
Ground Motion Model ◽  
Crustal Earthquakes ◽  
Magnitude Scaling ◽  
Finite Fault

Abstract We present a summary of the Bayless and Abrahamson (2018b) empirical ground‐motion model (GMM) for shallow crustal earthquakes in California based on the Next Generation Attenuation‐West2 database (Ancheta et al., 2014). This model is denoted as BA18. Rather than the traditional response spectrum GMM, BA18 is developed for the smoothed effective amplitude spectrum (EAS), as defined by the Pacific Earthquake Engineering Research Center (Goulet et al., 2018). The EAS is the orientation‐independent horizontal‐component Fourier amplitude spectrum of ground acceleration. The model is developed using a database dominated by California earthquakes but takes advantage of crustal earthquake data worldwide to constrain the magnitude scaling and geometric spreading. The near‐fault saturation is guided by finite‐fault numerical simulations, and nonlinear site amplification is incorporated using a modified version of Hashash et al. (2018). The model is applicable for rupture distances of 0–300 km, M 3.0–8.0, and over the frequency range 0.1–100 Hz. The model is considered applicable for VS30 in the range 180–1500  m/s, although it is not well constrained for VS30 values >1000  m/s. Models for the median and the aleatory variability of the EAS are developed. Regional models for Japan and Taiwan will be developed in a future update of the model. A MATLAB program that implements the EAS GMM is provided in the Ⓔ supplemental content to this article.

scholarly journals A Non-ergodic Spectral Acceleration Ground Motion Model for California Developed with Random Vibration Theory

2021 ◽  
Author(s):  
Grigorios Lavrentiadis ◽  
Norman A. Abrahamson
Keyword(s):  
Standard Deviation ◽  
Ground Motion ◽  
Random Vibration ◽  
Epistemic Uncertainty ◽  
Response Spectrum ◽  
Motion Model ◽  
Small Magnitude ◽  
Vibration Theory ◽  
Ground Motion Model ◽  
Random Vibration Theory

Abstract A new approach for creating a non-ergodic PSA ground-motion model (GMM) is presented which account for the magnitude dependence of the non-ergodic effects. In this approach, the average PSA scaling is controlled by an ergodic PSA GMM, and the non-ergodic effects are captured with non-ergodic PSA factors, which are the adjustment that needs to be applied to an ergodic PSA GMM to incorporate the non-ergodic effects. The non-ergodic PSA factors are based on EAS non-ergodic effects and are converted to PSA through Random Vibration Theory (RVT). The advantage of this approach is that it better captures the non-ergodic source, path, and site effects through the small magnitude earthquakes. Due to the linear properties of Fourier Transform, the EAS non-ergodic effects of the small events can be applied directly to the large magnitude events. This is not the case for PSA, as response spectrum is controlled by a range of frequencies, making PSA non-ergodic effects depended on the spectral shape which is magnitude dependent. Two PSA non-ergodic GMMs are derived using the ASK14 (Abrahamson et al., 2014) and CY14 (Chiou and Youngs, 2014) GMMs as backbone models, respectively. The non-ergodic EAS effects are estimated with the LAK21 (Lavrentiadis et al., In press) GMM. The RVT calculations are performed with the V75 (Vanmarcke, 1975) peak factor model, the Da0.05−0.85 estimate of AS96 (Abrahamson and Silva, 1996) for the ground-motion duration, and BT15 (Boore and Thompson, 2015) oscillator-duration model. The California subset of the NGAWest2 database (Ancheta et al., 2014) is used for both models. The total aleatory standard deviation of the two non-ergodic PSA GMMs is approximately 30 to 35% smaller than the total aleatory standard deviation of the corresponding ergodic PSA GMMs. This reduction has a significant impact on hazard calculations at large return periods. In remote areas, far from stations and past events, the reduction of aleatory variability is accompanied by an increase of epistemic uncertainty.

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.

scholarly journals A Non-Ergodic Effective Amplitude Ground-Motion Model for California

2021 ◽  
Author(s):  
Grigorios Lavrentiadis ◽  
Norman A. Abrahamson ◽  
Nicolas M. Kuehn
Keyword(s):  
Standard Deviation ◽  
Ground Motion ◽  
Epistemic Uncertainty ◽  
Amplitude Spectrum ◽  
Time History ◽  
Motion Model ◽  
Small Magnitude ◽  
Varying Coefficients ◽  
Ground Motion Model ◽  
Systematic Effects

Abstract A new non-ergodic ground-motion model (GMM) for effective amplitude spectral (EAS) values for California is presented in this study. EAS, which is defined in Goulet et al. (2018), is a smoothed rotation-independent Fourier amplitude spectrum of the two horizontal components of an acceleration time history. The main motivation for developing a non-ergodic EAS GMM, rather than a spectral acceleration GMM, is that the scaling of EAS does not depend on spectral shape, and therefore, the more frequent small magnitude events can be used in the estimation of the non-ergodic terms. The model is developed using the California subset of the NGAWest2 dataset Ancheta et al. (2013). The Bayless and Abrahamson (2019b) (BA18) ergodic EAS GMM was used as backbone to constrain the average source, path, and site scaling. The non-ergodic GMM is formulated as a Bayesian hierarchical model: the non-ergodic source and site terms are modeled as spatially varying coefficients following the approach of Landwehr et al. (2016), and the non-ergodic path effects are captured by the cell-specific anelastic attenuation attenuation following the approach of Dawood and Rodriguez-Marek (2013). Close to stations and past events, the mean values of the non-ergodic terms deviate from zero to capture the systematic effects and their epistemic uncertainty is small. In areas with sparse data, the epistemic uncertainty of the non-ergodic terms is large, as the systematic effects cannot be determined. The non-ergodic total aleatory standard deviation is approximately 30 to 40% smaller than the total aleatory standard deviation of BA18. This reduction in the aleatory variability has a significant impact on hazard calculations at large return periods. The epistemic uncertainty of the ground motion predictions is small in areas close to stations and past event.

A ground motion model for volcanic areas in Italy

2019 ◽  
Vol 18 (1) ◽  
pp. 57-76 ◽  
Author(s):  
Giovanni Lanzano ◽  
Lucia Luzi
Keyword(s):  
Ground Motion ◽  
Motion Model ◽  
Ground Motion Model
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