Correlation of Arias intensity with amplitude, duration and cumulative intensity measures

Bradley, Brendon

doi:10.1016/j.soildyn.2015.07.009

Cited by 51 publications

(40 citation statements)

References 44 publications

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“…Based on the obtained results, it can be seen that having an appropriate representation for the cumulative effects of ground motions for a scenario rupture does not necessarily guarantee an appropriate representation for significant duration IMs of the motions. This is consistent with the results of Bradley and Bradley who found a near‐zero correlation between the residual of significant duration IMs (i.e. D s575 or D s595 ) and AI and CAV and the results of Bommer et al .…”

Section: Application Of the Gcim Methodology For Scenario Sha‐based Gsupporting

confidence: 93%

“…As seen in this figure, there is a proper representation for CAV (as expected), and also, there is an unbiased distribution of AI. In this case, the unbiased representation of the AI distribution (observed for most of the considered scenarios and site conditions in Table ) can be related to the strong correlation of AI with short‐period SA ordinates , which are relatively well represented by the implemented weight vector (i.e. non‐zero weights for 18 SA ordinates).…”

Section: Application Of the Gcim Methodology For Scenario Sha‐based Gmentioning

confidence: 92%

“… for D s575 and D s595 . Also, the empirical correlation equations and the corresponding values between the considered IMs based on Baker and Jayaram and Bradley are presented in Table .…”

Section: Application Of the Gcim Methodology For Scenario Sha‐based Gmentioning

confidence: 99%

“…Equations are functions of vibration period and given by the following references: BJ08 in Baker and Jayaram , B11(b) in Bradley , B12(b) in Bradley , B11(a) in Bradley , B12(c) in Bradley , B11(c) in Bradley and B14(a) in Bradley .…”

Section: Application Of the Gcim Methodology For Scenario Sha‐based Gmentioning

confidence: 99%

“…Although not essential , the lognormal multivariate distribution is considered here for the joint distribution of IM based on its observed appropriateness in previous applications [e.g. ]. Based on this consideration, the marginal distribution of each IM, IM i , for the given scenario earthquake rupture, Rup , can be expressed as

f_{I M_{i} | R u p} ~ L N ((), μ_{l n I M_{i} | R u p}, σ_{l n I M_{i} | R u p}^{2})

where

f_{I M_{i} true| R u p}

is the probability density function of IM i given scenario rupture, that is, Rup ; X ~ LN ( ) is shorthand notation for X having a lognormal distribution, with

μ_{l n I M_{i} true| R u p}

and

σ_{l n I M_{i} (|, R u p)}^{2}

the corresponding mean and variance of lnIM i respectively.…”

Section: Gcim‐based Ground Motion Selection For Scenario Shamentioning

confidence: 99%

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Ground motion selection for scenario ruptures using the generalised conditional intensity measure (GCIM) method

Tarbali

Bradley

2015

Earthq Engng Struct Dyn

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Summary In this paper, the generalised conditional intensity measure (GCIM) method is extended to ground motion selection for scenario earthquake ruptures. The selection algorithm is based on generating random realisations of the considered intensity measure (IM) distributions for a specific rupture scenario and then finding the prospective ground motions that best fit the realisations using an optimal amplitude scale factor. Using different rupture scenarios and site conditions, two important aspects of the GCIM methodology are scrutinised: (i) different weight vectors for the various IMs considered and (ii) quantifying the importance of replicate selections for ensembles with different numbers of desired ground motions. It is demonstrated that considering only spectral acceleration (SA) ordinates in the selection process, as is common in many conventional selection procedures, may result in selected motions with a biased representation for duration and cumulative ground motion effects. In contrast, considering IMs other than SA ordinates (in particular, significant duration, cumulative absolute velocity, and Arias intensity) results in ensembles with an appropriate representation of these IMs, without a practically significant effect on SA ordinates. The benefit of conducting replicate selections to obtain a suite of motions with an improved representation for the distribution of the considered IMs is demonstrated, and a minimum number of replicates are suggested for different ground motion ensemble sizes. Copyright © 2015 John Wiley & Sons, Ltd.

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Section: Application Of the Gcim Methodology For Scenario Sha‐based Gsupporting

confidence: 93%

Section: Application Of the Gcim Methodology For Scenario Sha‐based Gmentioning

confidence: 92%

“… for D s575 and D s595 . Also, the empirical correlation equations and the corresponding values between the considered IMs based on Baker and Jayaram and Bradley are presented in Table .…”

Section: Application Of the Gcim Methodology For Scenario Sha‐based Gmentioning

confidence: 99%

Section: Application Of the Gcim Methodology For Scenario Sha‐based Gmentioning

confidence: 99%

f_{I M_{i} | R u p} ~ L N ((), μ_{l n I M_{i} | R u p}, σ_{l n I M_{i} | R u p}^{2})

where

f_{I M_{i} true| R u p}

is the probability density function of IM i given scenario rupture, that is, Rup ; X ~ LN ( ) is shorthand notation for X having a lognormal distribution, with

μ_{l n I M_{i} true| R u p}

and

σ_{l n I M_{i} (|, R u p)}^{2}

the corresponding mean and variance of lnIM i respectively.…”

Section: Gcim‐based Ground Motion Selection For Scenario Shamentioning

confidence: 99%

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Ground motion selection for scenario ruptures using the generalised conditional intensity measure (GCIM) method

Tarbali

Bradley

2015

Earthq Engng Struct Dyn

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Shaking table test and cumulative deformation evaluation analysis of a tunnel across the hauling sliding surface

Pai

Wu²,

2023

Deep Underground Science and Engineering

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To explore the cumulative deformation effect of the dynamic response of a tunnel crossing the hauling sliding surface under earthquakes, the shaking table test was conducted in this study. Combined with the numerical calculations, this study proposed magnification of the Arias intensity (MIa) to characterize the overall local deformation damage of the tunnel lining in terms of the deformation characteristics, frequency domain, and energy. Using the time‐domain analysis method, the plastic effect coefficient (PEC) was proposed to characterize the degree of plastic deformation, and the applicability of the seismic cumulative failure effect (SCFE) was discussed. The results show that the low‐frequency component (f1 and f2 ≤ 10 Hz) and the high‐frequency component (f3 and f4 > 10 Hz) acceleration mainly cause global and local deformation of the tunnel lining. The local deformation caused by the high‐frequency wave has an important effect on the seismic damage of the lining. The physical meaning of PEC is more clearly defined than that of the residual strain, and the SCFE of the tunnel lining can also be defined. The SCFE of the tunnel lining includes the elastic deformation effect stage (<0.15g), the elastic–plastic deformation effect stage (0.15g–0.30g), and the plastic deformation effect stage (0.30g–0.40g). This study can provide valuable theoretical and technical support for the construction of traffic tunnels in high‐intensity earthquake areas.

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Empirical correlations between ground motion intensity measures from the NGA‐West2 database and their use in vector generalized conditional intensity measures

Wang,

Wang

2024

Earthquake Engineering and Resilience

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This paper examines the empirical correlations between 14 intensity measures (IMs) describing the frequency content, amplitude, cumulative effects, and duration aspects of ground motion based on the NGA‐West2 database. The correlation results in this paper are compared with the results of previous models based on the NGA‐West1 database, and the previous correlation coefficient models are updated and extended. The comparison results show that the trend of the correlation coefficients of the model established in this paper is essentially consistent with previous models based on the NGA‐West1 database, with most correlation coefficients observed in this paper being slightly lower than in previous studies. In addition, this paper extends the generalized conditional intensity measure (GCIM) ground motion selection method so that it can consider multiple conditional IMs (VGCIM), and gives full theoretical details. The differences between the theories of VGCIM and GCIM are discussed, and several possible application scenarios of VGCIM are illustrated. An example application of VGCIM is shown, and the results show that there are deviations between the target IMs conditional distribution constructed after considering multiple conditional IMs and considering one IM that is sufficient to make an impact in the ground motion selection. Finally, the effect of the correlation coefficient model on the IMs conditional distributions generated based on GCIM and VGCIM is discussed.

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Correlation of Arias intensity with amplitude, duration and cumulative intensity measures

Cited by 51 publications

References 44 publications

Ground motion selection for scenario ruptures using the generalised conditional intensity measure (GCIM) method

Ground motion selection for scenario ruptures using the generalised conditional intensity measure (GCIM) method

Shaking table test and cumulative deformation evaluation analysis of a tunnel across the hauling sliding surface

Empirical correlations between ground motion intensity measures from the NGA‐West2 database and their use in vector generalized conditional intensity measures

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