New models for frequency content prediction of earthquake records based on Iranian ground-motion data

Yaghmaei‐Sabegh, Saman

doi:10.1007/s10950-015-9497-9

Cited by 18 publications

(15 citation statements)

References 24 publications

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“…Regarding the development of the GMPE for T m , we adopted the same functional form that was used by Rathje et al, excluding, as in the case of Yaghmaei‐Sabegh, the forward‐directivity term due to lack of such information in our dataset, ie,

ln T_{m} = a + b \cdot ((), M - 6) + c \cdot R + \sum_{i = 1, N} e_{i} \cdot s_{i} + \sum_{j = 1, M} f_{j} \cdot m_{j} + ε_{r} + ε_{e} .

…”

Section: Methodsmentioning

confidence: 99%

“…The most widely used relation is that of Rathje et al, which however was developed on the base of records including only a small number of events with M w < 5.5, and consequently, it should be used with caution below this limit. Recently, Yaghmaei‐Sabegh developed a model for T m prediction using data from earthquakes that occurred in Iran. As regards Greece, no predictive model for T m is available, and this is the first time that this parameter is incorporated in a GMPE.…”

Section: Ground‐motion Parameters Examinedmentioning

confidence: 99%

“…Trellis charts showing predicted peak horizontal acceleration (PHA), peak horizontal velocity (PHV), cumulative absolute velocity (CAV), and T m for different combinations of moment magnitude, style‐of‐faulting and site category. Legend abbreviations are as follows: S2004 = Skarlatoudis et al; D&T2007 = Danciu and Tselentis; A2014 = Akkar et al; C&B2010 = Campbell and Bozorgnia; D&W2013 = Du and Wang; S&A2017 = Sandıkkaya and Akkar; R2004 = Rathje et al; Y‐S2015 = Yaghmaei‐Sabegh…”

Section: Comparison With Other Gmpesmentioning

confidence: 99%

See 2 more Smart Citations

Regional ground‐motion prediction equations for amplitude‐, frequency response‐, and duration‐based parameters for Greece

Chousianitis

Gaudio

Pierri

et al. 2018

Earthq Engng Struct Dyn

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Summary Although all of the main properties of a ground motion cannot be captured through a single parameter, a number of different engineering parameters has been proposed that are able to reflect either one or more ground‐motion characteristics concurrently. For many of these parameters, especially regarding Greece, there are relatively few or no predictive models. In this context, we present a set of new regionally‐calibrated equations for the prediction of the geometric mean of the horizontal components of 10 amplitude‐, frequency response‐, and duration‐based parameters for shallow crustal earthquakes. These equations supersede previous empirical relationships for Greece since their applicability range for magnitude, and epicentral distance has been extended down to Mw 4 and up to 200 km, respectively, the incorporation of a term accounting for anelastic attenuation has been investigated, while their development was based on a ground‐motion dataset spanning from 1973 to 2014. For all ground‐motion parameters, we provide alternative optimal equations relative to the availability of information on the different explanatory variables. In all velocity‐based and contrary to the acceleration‐based parameters, the anelastic attenuation coefficient was found statistically insignificant when it was combined with the geometric decay and the coefficient accounting for saturation with distance. In the regressions where the geometric decay coefficient simultaneously incorporated the contribution of anelastic attenuation, its increase was found to be much less considerable in the velocity‐based than in the acceleration‐based parameters, implying a stronger effect of anelastic attenuation on the parameters that are defined via the acceleration time history.

show abstract

ln T_{m} = a + b \cdot ((), M - 6) + c \cdot R + \sum_{i = 1, N} e_{i} \cdot s_{i} + \sum_{j = 1, M} f_{j} \cdot m_{j} + ε_{r} + ε_{e} .

…”

Section: Methodsmentioning

confidence: 99%

Section: Ground‐motion Parameters Examinedmentioning

confidence: 99%

Section: Comparison With Other Gmpesmentioning

confidence: 99%

See 1 more Smart Citation

Regional ground‐motion prediction equations for amplitude‐, frequency response‐, and duration‐based parameters for Greece

Chousianitis

Gaudio

Pierri

et al. 2018

Earthq Engng Struct Dyn

View full text Add to dashboard Cite

show abstract

“…Accordingly, most presented models were developed based on these parameters (e.g., Rathje et al, 1998). The evaluation of the GMPEs shows that the models are generally functions of site effect, the magnitude and focal mechanism (e.g., Rathje et al, 2004;Yaghmaei-Sabegh, 2015;Du, 2017;Chousianitis et al, 2018). The combination of these parameters can provide an accurate model for prediction of T m .…”

Section: The Predictive Model Of T Mmentioning

confidence: 99%

“…Rathje et al (2004) developed a preceding model proposed by Rathje et al (1998) using an extended ground motion database and predictor variables. Yaghmaei-Sabegh (2015) proposed an empirical model using several Iranian earthquake ground motions. A database of global earthquake ground motions was employed by Du (2017Du ( , 2019 to develop a predictive model for T m .…”

Section: Introductionmentioning

confidence: 99%

Empirical model for frequency content estimation of strong ground motion records of Iran

Lashgari

Jafarian

2022

Engineering Geology

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Designing Fuzzy Controllers for Frame Structures Based on Ground Motion Prediction Using Grasshopper Optimization Algorithm: A Case Study of Tabriz, Iran

Azizi

2021

Springer Tracts in Nature-Inspired Computing

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New models for frequency content prediction of earthquake records based on Iranian ground-motion data

Cited by 18 publications

References 24 publications

Regional ground‐motion prediction equations for amplitude‐, frequency response‐, and duration‐based parameters for Greece

Regional ground‐motion prediction equations for amplitude‐, frequency response‐, and duration‐based parameters for Greece

Empirical model for frequency content estimation of strong ground motion records of Iran

Designing Fuzzy Controllers for Frame Structures Based on Ground Motion Prediction Using Grasshopper Optimization Algorithm: A Case Study of Tabriz, Iran

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