Semi-VariogramFunction for the Vertical Component of Iranian Acceleration Data

Document Type : Articles

Authors

1 International Institute of Earthquake Engineering and Seismology

2 University of Qom, Iran

3 Ale-Taha Institute of Higher Education, Tehran, Iran

Abstract

The evaluation of potential human and economic losses arising from earthquakes, which may affect urban infrastructures that are spatially extended over an area, is important for nationalauthorities, local municipalities, and the insurance and reinsurance industries. However, seismic-risk analysis of distributed systems and infrastructures need to apply a different approach with respect to the classical site-specific hazard and risk analysis. Ground motion intensity measures (IMs) and resulting structural responses are correlated in neighborhood sites. The correlation value depends on the distance between the adjacent sites and the natural vibration period of structures. In particular, when a lifeline system is of concern, classical site-specific hazard tools, which consider IMs at different locations independently, may not be accurate enough to assess the seismic risk. In fact, modeling of ground motion as a random field, which consists of assigning a spatial correlation to the IM of interest, is required. It is very common in the seismic design of spatially distributed structures and lifelines to include the correlation of the nearby earthquake records, through empirical semi-variogram functions. In this study, the semi-variogram of vertical components as a function of inter-site separation distance with respect to the ground motion prediction equations for the Iranian acceleration data (vertical peak ground acceleration (PGA) and vertical pseudo spectral acceleration (PSA)) are presented for the first time using acceleration data from 220 earthquakes. The calculations were carried out for five natural vibration periods in the range of 0 to 3 seconds and using ground motion prediction equations for vertical component. The selected ground motion prediction equation is the local model proposed by Soghrat & Ziaeifar (2017). For estimation ofempirical semi-variogram, two classical and robust estimators, and to fit the data, the exponential and Goda models are used. For the ground motion prediction equation by Soghrat & Zyiaeifar (2017), the values of the range (b) in the exponential model andthe values of α and β in the model of Goda (i.e. a continuous function fitted to experimental values in order to deduce semivariogram values for any possible site separation distance, Goda & Hong, 2008) are estimated. It is observed that the correlation trend range generally increases with period.

Keywords

References

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History

  • Receive Date: 19 August 2017
  • Revise Date: 25 February 2021
  • Accept Date: 26 December 2020

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