Towards fully data driven ground-motion prediction models for Europe

Derras, Boumédiène; Bard, Pierre Yves; Cotton, Fabrice

doi:10.1007/s10518-013-9481-0

Cited by 123 publications

(91 citation statements)

References 24 publications

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“…Selecting models based on multiple approaches is, however, a way towards more effectively capturing epistemic uncertainty in terms of the centre, the body and the range of technically-defensible interpretations of the available data (USNRC 2012). The main ''European'' branch of the logic tree includes for this reason two models based on the classical random-effects approach (Akkar et al 2014;Bindi et al 2014) but also a model based on the neural-network, data-driven and calibration method (Derras et al 2014). Equal weights were assigned to these three.…”

Section: Taking Into Account Functional Form Variationsmentioning

confidence: 99%

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The probabilistic seismic hazard assessment of Germany—version 2016, considering the range of epistemic uncertainties and aleatory variability

Grünthal

Stromeyer

Bosse

et al. 2018

Bull Earthquake Eng

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The basic seismic load parameters for the upcoming national design regulation for DIN EN 1998-1/NA result from the reassessment of the seismic hazard supported by the German Institution for Civil Engineering (DIBt). This 2016 version of the national seismic hazard assessment for Germany is based on a comprehensive involvement of all accessible uncertainties in models and parameters and includes the provision of a rational framework for integrating ranges of epistemic uncertainties and aleatory variabilities in a comprehensive and transparent way. The developed seismic hazard model incorporates significant improvements over previous versions. It is based on updated and extended databases, it includes robust methods to evolve sets of models representing epistemic uncertainties, and a selection of the latest generation of ground motion prediction equations. The new earthquake model is presented here, which consists of a logic tree with 4040 end branches and essential innovations employed for a realistic approach. The output specifications were designed according to the user oriented needs as suggested by two review teams supervising the entire project. Seismic load parameters, for rock conditions of v S30 = 800 m/s, are calculated for three hazard levels (10, 5 and 2% probability of occurrence or exceedance within 50 years) and delivered in the form of uniform hazard spectra, within the spectral period range 0.02-3 s, and seismic hazard maps for peak

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Section: Taking Into Account Functional Form Variationsmentioning

confidence: 99%

“…In summary, the RESOURCE data branch of the logic tree is represented by the models of Akkar et al (2014), Bindi et al (2014) and Derras et al (2014). The NGA-West-2 database branch is considered by the GMPE of Bindi et al (2017) and the mainly Japanese based data by Cauzzi et al (2015).…”

Section: Selected Gmpes and Their Model Parametersmentioning

confidence: 99%

The probabilistic seismic hazard assessment of Germany—version 2016, considering the range of epistemic uncertainties and aleatory variability

Grünthal

Stromeyer

Bosse

et al. 2018

Bull Earthquake Eng

Self Cite

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“…In particular, in many fields of science, data-driven approaches have shown their suitability for generating models with high predictive powers (e.g., Breiman, 2001). In the context of GMPE calibration, examples of data-driven approaches include the application of artificial neural networks (e.g., Derras et al, 2012Derras et al, , 2014. To exemplify their application to the case study at hand, Figure 2 shows a simple neural network that includes one hidden layer with three neurons.…”

Section: Discussionmentioning

confidence: 99%

The Predictive Power of Ground‐Motion Prediction Equations

Bindi

2017

Bulletin of the Seismological Society of America

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Although model calibration should be always performed alongside model validation, this is rarely the case when deriving new ground-motion prediction equations (GMPEs). Explanatory modeling (Shmueli, 2010) is often preferred to data-driven predictive approaches, and the analysis of the residual distribution is generally used to support the model qualification. Following previous studies (Kuehn et al., 2009;Scherbaum et al., 2009), this work aims to again stress the importance of validation for assessing the predictive power of GMPEs and for avoiding, or at least limiting, data overfitting. Considering the strong-motion data and models recently analyzed by Roselli et al. (2016), I exemplify the application of standard validation approaches based on predictive metrics (Akaike and Bayesian information criteria), resample techniques (cross validation and bootstrap), and validation against new data. The results confirm that GMPEs overfitting the calibration data have a limited predictive power, whereas, regarding validation against new data, the selection of the validation data set should take into account possible regional effects in the ground motion.

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“…Following this five sets of developers present their models derived using RESORCE. Two of the groups (Akkar et al, 2013a, b;Bindi et al, 2013) use random-effects regression analysis, whereas Hermkes et al (2013), Bora et al (2013) and Derras et al (2013) develop their models using novel techniques. Sandıkkaya et al (2013) extend the model developed by Akkar et al (2013a, b), for the prediction of the horizontal component of earthquake response spectra for 5% of critical damping, to other damping levels and for the prediction of the vertical component of motion.…”

Section: Preface Of Special Issue On: a New Generation Of Ground-motimentioning

confidence: 99%

Preface of special issue: A new generation of ground-motion models for Europe and the Middle East

Douglas

2013

Bull Earthquake Eng

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Towards fully data driven ground-motion prediction models for Europe

Abstract: International audienc

Cited by 123 publications

References 24 publications

The probabilistic seismic hazard assessment of Germany—version 2016, considering the range of epistemic uncertainties and aleatory variability

The probabilistic seismic hazard assessment of Germany—version 2016, considering the range of epistemic uncertainties and aleatory variability

The Predictive Power of Ground‐Motion Prediction Equations

Preface of special issue: A new generation of ground-motion models for Europe and the Middle East

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