SELECTION OF THE MOST APPROPRIATE GROUND MOTION PREDICTION EQUATION FOR LOCAL SEISMIC HAZARD ANALYSIS

Yıl 2020, Cilt: 21 Sayı: 1, 21 - 38, 31.03.2020

Hakan Karaca

https://doi.org/10.18038/estubtda.514731

Öz

Together with the ever-increasing number of global and
local Ground Motion Prediction Equations (GMPEs) and the complexity of the
functional forms, incompatibility problems arise in the selection of the most
appropriate GMPE for a specific location. Obviously, associated with the
incompatibility issues, practitioners face a compromise over the precision of
prediction because the functional form of the considered GMPE might be
developed by considering all the influential parameters, which might not be
available for the considered location. Hence, a modification is required to
adjust the considered GMPE to local conditions by using the local ground motion
data. The sensitivity of the parameters of the selected GMPEs to the local
seismic propagation patterns can be determined only after the adjustment. The
local propagation patterns, on the other hand, can only be identified by
analyzing the indigenous data. Together with the attempts to solve the
incompatibility problem, the selection of the most appropriate GMPE becomes the
selection of the most suitable functional form.

The aim of this study is to select the most
appropriate GMPE for Eskişehir
through the guidance of the above statements. A number of GMPEs are selected
according to the criteria of wider utilization and recognition. All the
candidate GMPEs were subjected to adjustments, including some minor
modifications and the calibration of the coefficients by using the indigenous
data. Then, a number of statistical and visual procedures were applied including
the performance test of the adjusted GMPEs with the records of the two largest
earthquakes that occurred in the region. The study highlights the influence of
the local seismic behavior on the performance of various functional forms of
the candidate GMPEs. 

Anahtar Kelimeler

Eskişehir, Ground Motion Prediction Equation, LLH method, EDR method, Residual Analysis

Kaynakça

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