Sinan Akkar

Empirical Prediction Equations for Peak Ground Velocity Derived from Strong-Motion Records from Europe and the Middle East

Peak ground velocity (PGV) has many applications in earthquake engineering, but there are relatively few prediction equations for this parameter in comparison with the large numbers of equations for estimating peak ground accel- eration and response spectral ordinates. This lack of empirical equations for PGV has led to widespread use of the practice of scaling peak velocity from the 5%-damped response spectral ordinate at 1 sec, which is a poor substitute for direct prediction of the parameter. Responding to the need to provide equations for the prediction of PGV, this article derives new equations using the strong-motion database for the seismically active areas of Europe and the Middle East, following a new processing of all of the records. A total of 532 strong-motion accelerograms recorded at distances of up to 100 km from 131 earthquakes with moment magnitudes ranging from M 5 to 7.6 are used to derive equations for both the larger and the geometric mean of the horizontal components. The predictions are found to be broadly consistent with those from previous European equations, and also with preliminary results from the Next Generation of Attenuation (NGA) project, suggesting that systematic differences in ground motions from active crustal regions, if any, are sufficiently small not to prevent the combined use of strong-motion data from southern Europe, western North America, and other tectonically active areas of shallow crustal seismicity.

The Influence of Magnitude Range on Empirical Ground-Motion Prediction

A key issue in the assessment of seismic hazard in regions of low- to-moderate seismicity is the extent to which accelerograms obtained from small- magnitude earthquakes can be used as the basis for predicting ground motions due to the larger-magnitude events considered in seismic hazard analysis. In essence, the question is whether empirical ground-motion prediction equations can be applied outside their strict range of applicability as defined by the magnitude and distance ranges covered by the datasets from which they are derived. This question is explored by deriving new spectral prediction equations using an extended strong-motion da- taset from Europe and the Middle East covering the magnitude range Mw 3.0-7.6 and comparing the predictions with previous equations derived using data from only Mw 5.0 and above events. The comparisons show that despite their complex func- tional form, including quadratic magnitude-dependence and magnitude-dependent attenuation, the equations derived from larger-magnitude events should not be extra- polated to predict ground motions from earthquakes of small magnitude. Moreover, the results suggest not only that ground-motion prediction equations cannot be used outside the ranges of their underlying datasets but also that their applicability at the limits of these ranges may be questionable. Although only tested for smaller magni- tudes, the results could be interpreted to suggest that predictive equations also cannot be reliably extrapolated to higher magnitudes than those represented in the dataset from which they are derived, a finding that has important implications for seismic hazard analysis. The conclusion of the study is that empirical derivation of ground-motion pre- diction equations should be based on datasets extending at least one unit below the lower limit of magnitude considered in seismic hazard calculations. The inclusion of small-magnitude recordings results in a significant increase in the aleatory varia- bility of the equations, although it is yet to be established whether this is due to greater uncertainty in the associated metadata or whether ground-motion variability is gen- uinely dependent on earthquake magnitude.

Toward a Ground-Motion Logic Tree for Probabilistic Seismic Hazard Assessment in Europe

The Seismic Hazard Harmonization in Europe (SHARE) project, which began in June 2009, aims at establishing new standards for probabilistic seismic hazard assessment in the Euro-Mediterranean region. In this context, a logic tree for ground-motion prediction in Europe has been constructed. Ground-motion prediction equations (GMPEs) and weights have been determined so that the logic tree captures epistemic uncertainty in ground-motion prediction for six different tectonic regimes in Europe. Here we present the strategy that we adopted to build such a logic tree. This strategy has the particularity of combining two complementary and independent approaches: expert judgment and data testing. A set of six experts was asked to weight pre-selected GMPEs while the ability of these GMPEs to predict available data was evaluated with the method of Scherbaum et al. (Bull Seismol Soc Am 99:3234–3247, 2009). Results of both approaches were taken into account to commonly select the smallest set of GMPEs to capture the uncertainty in ground-motion prediction in Europe. For stable continental regions, two models, both from eastern North America, have been selected for shields, and three GMPEs from active shallow crustal regions have been added for continental crust. For subduction zones, four models, all non-European, have been chosen. Finally, for active shallow crustal regions, we selected four models, each of them from a different host region but only two of them were kept for long periods. In most cases, a common agreement has been also reached for the weights. In case of divergence, a sensitivity analysis of the weights on the seismic hazard has been conducted, showing that once the GMPEs have been selected, the associated set of weights has a smaller influence on the hazard.

A Local Ground-Motion Predictive Model for Turkey, and Its Comparison with Other Regional and Global Ground-Motion Models

We examined the differences between the ground-motion estimations of local and global prediction equations and explored some seismological parameters that may explain these differences. To achieve this objective, we first derived a set of ground-motion prediction equations (GMPEs) for estimating peak horizontal accel- eration, velocity, and pseudospectral acceleration using the recently compiled Turkish ground-motion database. The new GMPEs are comparable with the recent global GMPEs in terms of model sophistication, and they are based on a well-studied national dataset. Using global GMPEs from the Next Generation Attenuation of Ground Motions project (Power et al., 2008) and the pan-European Akkar and Bommer (2010) model, we observed that the discrepancy between local and global GMPEs is more prominent at small magnitudes provided that the GMPEs possess similar mag- nitude limits. Our more detailed comparisons with the pan-European Akkar and Bom- mer (2010) predictive model, as well as with the estimations from a combined Italian and Turkish accelerometric dataset, indicate that depth can be of importance for delineating the differences between local and global GMPEs.

Displacement-Based Fragility Functions for Low- and Mid-rise Ordinary Concrete Buildings

Fragility functions are determined for low- and mid-rise ordinary concrete buildings, which constitute the most vulnerable construction type in Turkey as well as several other countries prone to earthquakes. A hybrid approach is employed where building capacities are obtained from field data and their dynamic responses are calculated by response history analyses. Field data consists of 32 sample buildings representing the general characteristics of two- to five-story substandard reinforced concrete buildings in Turkey. Lateral stiffness, strength, and deformation capacities of the sample buildings are determined by pushover analyses conducted in two principal directions. Uncertainties in lateral stiffness, strength, and damage limit states are expressed by using statistical distributions. The inelastic dynamic structural characteristics of the buildings investigated are represented by a family of equivalent single-degree-of-freedom systems and their seismic deformation demands are calculated under 82 ground-motion records. Peak ground velocity (PGV) is selected as the measure of seismic intensity since maximum inelastic displacements are better correlated with PGV than peak ground acceleration (PGA). Fragility functions are derived separately for different number of stories, which is a prominent parameter influencing the vulnerability of existing substandard concrete buildings.

A Model for Single‐Station Standard Deviation Using Data from Various Tectonic Regions

Correctly accounting for the uncertainty in ground‐motion prediction is a critical component of probabilistic seismic‐hazard analysis (PSHA). This prediction is commonly achieved using empirical ground‐motion prediction equations. The differences between the observed and predicted ground‐motion parameters are generally assumed to follow a normal distribution with a mean of zero and a standard deviation sigma. Recent work has focused on the development of partially nonergodic PSHA, where the repeatable effects of site response on ground‐motion parameters are removed from their total standard deviation. The resulting value is known as single‐station standard deviation or single‐station sigma. If event‐to‐event variability is also removed from the single‐station standard deviation, the resulting value is referred to as the event‐corrected single‐station standard deviation (![Graphic][1] ). In this work, a large database of ground motions from multiple regions is used to obtain global estimates of these parameters. Results show that the event‐corrected single‐station standard deviation is remarkably stable across tectonic regions. Various models for this parameter are proposed accounting for potential magnitude and distance dependencies. The article also discusses requirements for using single‐station standard deviation in a PSHA. These include the need for an independent estimate of the site term (e.g., the repeatable component of the ground‐motion residual at a given station) and properly accounting for the epistemic uncertainty in both the site term and the site‐specific single‐station standard deviation. Values for the epistemic uncertainty on ![Graphic][2] are proposed based on the station‐to‐station variability of this parameter. [1]: /embed/inline-graphic-1.gif [2]: /embed/inline-graphic-2.gif

A New Procedure for Selecting and Ranking Ground-Motion Prediction Equations (GMPEs): The Euclidean Distance-Based Ranking (EDR) Method

Abstract We introduce a procedure for selecting and ranking of ground‐motion prediction equations (GMPEs) that can be useful for regional or site‐specific probabilistic seismic hazard assessment (PSHA). The methodology is called Euclidean distance‐based ranking (EDR) as it modifies the Euclidean distance ( DE ) concept for ranking of GMPEs under a given set of observed data. DE is similar to the residual analysis concept; its modified form, as discussed in this paper, can efficiently serve for ranking the candidate GMPEs. The proposed procedure separately considers ground‐motion uncertainty (i.e., aleatory variability addressed by the standard deviation) and the bias between the observed data and median estimations of candidate GMPEs (i.e., model bias). Indices computed from the consideration of aleatory variability and model bias or their combination can rank GMPEs to design GMPE logic trees that can serve for site‐specific or regional PSHA studies. We discussed these features through a case study and ranked a suite of GMPEs under a specific ground‐motion database. The case study indicated that separate consideration of ground‐motion uncertainty (aleatory variability) and model bias or their combination can change the ranking of GMPEs, which also showed that the ground‐motion models having simpler functional forms generally rank at the top of the list. We believe that the proposed method can be a useful tool to improve the decision‐making process while identifying the most proper GMPEs according to the specific objectives of PSHA. Online Material: MATLAB script and sample input file for EDR index calculation.

Comparisons among the five ground-motion models developed using RESORCE for the prediction of response spectral accelerations due to earthquakes in Europe and the Middle East

This article presents comparisons among the five ground-motion models described in other articles within this special issue, in terms of data selection criteria, characteristics of the models and predicted peak ground and response spectral accelerations. Comparisons are also made with predictions from the Next Generation Attenuation (NGA) models to which the models presented here have similarities (e.g. a common master database has been used) but also differences (e.g. some models in this issue are nonparametric). As a result of the differing data selection criteria and derivation techniques the predicted median ground motions show considerable differences (up to a factor of two for certain scenarios), particularly for magnitudes and distances close to or beyond the range of the available observations. The predicted influence of style-of-faulting shows much variation among models whereas site amplification factors are more similar, with peak amplification at around 1s. These differences are greater than those among predictions from the NGA models. The models for aleatory variability (sigma), however, are similar and suggest that ground-motion variability from this region is slightly higher than that predicted by the NGA models, based primarily on data from California and Taiwan.

Consistent Source-to-Site Distance Metrics in Ground-Motion Prediction Equations and Seismic Source Models for PSHA

Most modern ground-motion prediction equations (GMPE) use definitions of the source-to-site distance that reflect the dimensions of the fault rupture for larger earthquakes rather than using point-source measures relative to the epicenter or hypocenter. This is a positive development since it more realistically reflects the fact that energy is released from the crust around the entire fault rupture during a large earthquake. However, seismic source configurations defined for probabilistic seismic hazard analysis (PSHA) almost invariably include areas of distributed point-source seismicity in addition to linear fault sources, particularly in regions of lower earthquake activity. Herein, two GMPEs are derived from the same dataset to demonstrate the errors that can result from combining point-source simulations and extended-source distance metrics. The case is made for all ground-motion model developers to consider deriving pairs of equations, one using an extended-source distance metric, the other a point-source measure.

A Model for Vertical-to-Horizontal Response Spectral Ratios for Europe and the Middle East

In the framework of probabilistic seismic hazard analysis, the preferred approach for obtaining the response spectrum of the vertical component of motion is to scale the horizontal spectrum by vertical-to-horizontal (V/H) spectral ratios. In order to apply these ratios to scenario or conditional mean spectra, the V/H ratios need to be defined as a function of variables such as magnitude, distance, and site classification. A new model for the prediction of V/H ratios for peak ground acceleration and spectral accelerations from 0.02 to 3.0 s is developed from the database of strong-motion accelerograms from Europe and the Middle East. A simple functional form, expres- sing the V/H ratios as a function of magnitude, style of faulting, distance, and site class, is found to be appropriate, and the associated aleatory variability is found to be at least as low as that obtained in other studies using more complex models. The predicted ratios from the new European model are found to be in broad agreement with recent models derived from predominantly western North America data.