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Earthquakes and Structures   Volume 14, Number 2, February 2018, pages 129-142
DOI: http://dx.doi.org/10.12989/eas.2018.14.2.129
 
Maximum damage prediction for regular reinforced concrete frames under consecutive earthquakes
Gholamreza Ghodrati Amiri and Elham Rajabi

 
Abstract     [Full Text]
    The current paper introduces a new approach for development of damage index to obtain the maximum damage in the reinforced concrete frames caused by as-recorded single and consecutive earthquakes. To do so, two sets of strong ground motions are selected based on maximum and approximately maximum peak ground acceleration (PGA) from \"PEER\" and \"USGS\" centers. Consecutive earthquakes in the first and second groups, not only occurred in similar directions and same stations, but also their real time gaps between successive shocks are less than 10 minutes and 10 days, respectively. In the following, a suite of six concrete moment resisting frames, including 3, 5, 7, 10, 12 and 15 stories, are designed in OpenSees software and analyzed for more than 850 times under two groups of as-recorded strong ground motion records with/without seismic sequences phenomena. The idealized multilayer artificial neural networks, with the least value of Mean Square Error (MSE) and maximum value of regression (R) between outputs and targets were then employed to generate the empirical charts and several correction equations for design utilization. To investigate the effectiveness of the proposed damage index, calibration of the new approach to existing real data (the result of Park-Ang damage index 1985), were conducted. The obtained results show good precision of the developed ANNs-based model in predicting the maximum damage of regular reinforced concrete frames.
 
Key Words
    seismic sequence; damage index; reinforced concrete frames; empirical charts; artificial neural networks
 
Address
Gholamreza Ghodrati Amiri and Elham Rajabi: Center of Excellence for Fundamental Studies in Structural Engineering, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran
 

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