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Earthquakes and Structures Volume 26, Number 2, February 2024 , pages 117-130 DOI: https://doi.org/10.12989/eas.2024.26.2.117 |
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Bayesian approach for the accuracy evaluating of the seismic demand estimation of SMRF |
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Ayoub Mehri Dehno, Hasan Aghabarati and Mehdi Mahdavi Adeli
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| Abstract | ||
| Probabilistic model of seismic demand is the main tool used for seismic demand estimation, which is a fundamental component of the new performance-based design method. This model seeks to mathematically relate the seismic demand parameter and the ground motion intensity measure. This study is intended to use Bayesian analysis to evaluate the accuracy of the seismic demand estimation of Steel moment resisting frames (SMRFs) through a completely Bayesian method in statistical calculations. In this study, two types of intensity measures (earthquake intensity-related indices such as magnitude and distance and intensity indices related to ground motion and spectral response including peak ground acceleration (PGA) and spectral acceleration (SA)) have been used to form the models. In addition, an extensive database consisting of sixty accelerograms was used for time-series analysis, and the target structures included five SMRFs of three, six, nine, twelve and fifteen stories. The results of this study showed that for low-rise frames, first mode spectral acceleration index is sufficient to accurately estimate demand. However, for high-rise frames, two parameters should be used to increase the accuracy. In addition, adding the product of the square of earthquake magnitude multiplied by distance to the model can significantly increase the accuracy of seismic demand estimation. | ||
| Key Words | ||
| Bayesian analysis; EDP; nonlinear dynamic analysis; PSDM; seismic characteristics; SMRF | ||
| Address | ||
| Ayoub Mehri Dehno and Hasan Aghabarati: Department of Civil Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran Mehdi Mahdavi Adeli: Department of Civil Engineering, Shoushtar Branch, Islamic Azad University, Shoushtar, Iran | ||