Simulation of orthogonal horizontal components of near‐fault ground motion for specified earthquake source and site characteristics

Dabaghi, Mayssa; Kiureghian, Armen Der

doi:10.1002/eqe.3021

Cited by 47 publications

(61 citation statements)

References 52 publications

(194 reference statements)

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“…The velocity-pulse model proposed by Dabaghi and Der Kiureghian (2018), which describes near-field earthquake ground motions, defines the excitation a g (t). The velocity-pulse model consists of a cosine carrier wave with a period T p , modulated by a truncated cosine function, namely the pulse function, with a period γT p and amplitude V p .…”

Section: Prototype Structure and Excitationmentioning

confidence: 99%

A global sensitivity analysis framework for hybrid simulation

Abbiati

Marelli

Tsokanas

et al. 2021

Mechanical Systems and Signal Processing

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Hybrid Simulation is a dynamic response simulation paradigm that merges physical experiments and computational models into a hybrid model. In earthquake engineering, it is used to investigate the response of structures to earthquake excitation. In the context of response to extreme loads, the structure, its boundary conditions, damping, and the ground motion excitation itself are all subjected to large parameter variability. However, in current seismic response testing practice, Hybrid Simulation campaigns rely on a few prototype structures with fixed parameters subjected to one or two ground motions of different intensity. While this approach effectively reveals structural weaknesses, it does not reveal the sensitivity of structure's response. This thus far missing information could support the planning of further experiments as well as drive modeling choices in subsequent analysis and evaluation phases of the structural design process. This paper describes a Global Sensitivity Analysis framework for Hybrid Simulation.This framework, based on Sobol' sensitivity indices, is used to quantify the sensitivity of the response of a structure tested using the Hybrid Simulation approach due to the variability of the prototype structure and the excitation parameters. Polynomial Chaos Expansion is used to surrogate the hybrid model response. Thereafter, Sobol' sensitivity indices are obtained as a by-product of polynomial coefficients, entailing a reduced number of Hybrid Simulations compared to a crude Monte Carlo approach. An experimental verification example highlights the excellent performance of Polynomial Chaos Expansion surrogates in terms of stable estimates of Sobol' sensitivity indices in the presence of noise caused by random experimental errors.

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Section: Prototype Structure and Excitationmentioning

confidence: 99%

A global sensitivity analysis framework for hybrid simulation

Abbiati

Marelli

Tsokanas

et al. 2021

Mechanical Systems and Signal Processing

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“…Use the obtained information of EP to simulate multiple sets of MP . This is done using the predictive models proposed by Rezaeian and Der Kiureghian 12 (for R rup

>

30 km) and Dabaghi and Der Kiureghian 11 (for R rup

\leq

30 km), which relate the MP with the EP . In the simulated set of MP , select the important MP I as per the period range T and use the proposed log‐scaled predictive relations (as per Table 2) between the MP I and ln (

RotD 50 {boldS}_{bolda} (T) false)

to independently estimate mean

\bar{\ln false(RotD 50 {boldS}_{bolda} false(boldT false) false)}

and variances

{σ_{\ln (boldRotD 50 S_{a} (T))}}^{2}

of RotD50S a for each period in T . Use

{σ_{\ln (boldRotD 50 S_{a} (T))}}^{2}

to develop the diagonal variance matrix D . Use the developed variance matrix (D ) and lower triangular correlation matrix ( L ) corresponding to the periods T to transform the target spectrum (

RotD 50 {boldS}_{a, Tar} (T) false)

to the standard normal domain (

U_{boldRotD 50 S_{bolda, boldTar} (T)}

) using Equation ().…”

Section: Proposed Algorithmmentioning

confidence: 99%

An efficient algorithm to simulate site‐based ground motions that match a target spectrum

Fayaz

Zareian

2021

Earthq Engng Struct Dyn

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An efficient algorithm is presented to simulate site‐based ground motions using Dabaghi and Der Kiureghian (2018) and Rezaeian and Der Kiureghian (2012) that achieve target ground motion intensity measures between a range of periods. The proposed algorithm uses predictive relations between the model parameters (MP) and the RotD50 spectral acceleration spectrum, RotD50Sa,Tar(T), of the site‐based ground motions. A correlation structure is estimated to account for the cross‐spectral correlations between the periods. The algorithm requires a set of five seismic event parameters (including Magnitude, Rupture Distance, Soil Shear‐Wave Velocity, Fault Mechanism, Fault Geometry) and the target spectrum (including the range of periods whose corresponding spectral accelerations are required to be matched). The proposed algorithm uses event parameters to generate appropriate sets of model parameters. Using the developed predictive relations and correlation structure, the algorithm then carefully selects the model parameters that are statistically likely to generate a ground motion waveform possessing the required target spectrum between the inputted range of periods. Unlike the traditional scaling methods, this algorithm does not alter the amplitude, time‐ and frequency‐domain characteristics of the ground motions; hence, it leads to realistic simulated ground motions. The algorithm is used to generate ground motions for response assessment of a two‐span ordinary box‐girder bridge structure, and the results are compared against the traditional methods of selecting and scaling recorded ground motions. This demonstration indicates that the conventional methods of selecting and scaling recorded ground motions lead to a bias in the bridge's responses and the proposed algorithm leads to statistical consistency in the results.

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“…This model is based on a modulated and filtered white-noise process and can be used to generate ground motion time series for given Event Parameters. The work of Rezaeian and Der Kiureghian (2012) was extended by Dabaghi and Der Kiureghian (2018) to simulate near-fault time series. To account for the directivity effects observed in near-fault ground motions, this site-based model generates both pulse-like and non-pulse-like ground motions using the pulse probability model by Shahi and Baker (2014).…”

Section: Drd Ground Motion Simulation Modelmentioning

confidence: 99%

“…Several recent studies have turned to simulated ground motions for performance-based earthquake engineering applications. For example, a Caltrans research team (Yoon et al, 2019) used the concept of Probabilistic Damage Control Application (PDCA) to analyze bridge structures using a site-based ground motion simulation model developed by Dabaghi and Der Kiureghian (2018). The study used the Event Parameters of the top three contributing rupture sources to simulate ground motions and then either conduct point scaling to match S a ( T 1 ) of the UHS or use a range scaling method to match S a ( T 1 ± 1 s) of the UHS.…”

Section: Introductionmentioning

confidence: 99%

An efficient algorithm to simulate hazard-targeted site-based synthetic ground motions

et al. 2020

Self Cite

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This study presents an efficient algorithm that can be used to simulate ground motion waveforms using the site-based approach developed by Dabaghi and Der Kiureghian, and Rezaeian and Der Kiureghian that not only correspond to a specified seismic scenario (e.g. magnitude, distance, site conditions) but are also certain to achieve a target ground motion intensity measure within a narrow range. The suggested algorithm alleviates the need to scale simulated ground motions generated using the above-mentioned site-based approach; the resulting hazard-targeted simulated ground motions have consistent amplitude and time- and frequency-domain characteristics, which are required for proper seismic demand analysis of structures. The proposed algorithm takes as input a set of seismic Event Parameters and the target hazard intensity measure [Formula: see text] and generates a corresponding set of Model Parameters (i.e. input to the site-based ground motion simulation model). These Model Parameters are then used to simulate ground motion waveforms that not only represent the set of input Event Parameters ( Mw, Rrup, Vs30) but also maintain the target [Formula: see text]. To generate the set of Model Parameters, predictive relations between the Model Parameters and [Formula: see text] of ground motions are developed. Among the Model Parameters, the ones classified as important by statistical procedures (such as Random Forests, Forward Selection) are used to develop the predictive relations. The developed relations are then validated against a large dataset of recorded ground motions. The final implementation is provided in terms of graphic-user interface (GUI) called “Hazard-Targeted Time-Series Simulator” ( HATSim), which efficiently simulates site-based ground motions with minimum inputs.

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Simulation of orthogonal horizontal components of near‐fault ground motion for specified earthquake source and site characteristics

Cited by 47 publications

References 52 publications

A global sensitivity analysis framework for hybrid simulation

A global sensitivity analysis framework for hybrid simulation

An efficient algorithm to simulate site‐based ground motions that match a target spectrum

An efficient algorithm to simulate hazard-targeted site-based synthetic ground motions

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