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首页> 外文期刊>Earthquake engineering & structural dynamics >Unbiased rank selection for automatic hysteretic response extraction of RC frame buildings using acceleration recordings for post-earthquake safety evaluations
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Unbiased rank selection for automatic hysteretic response extraction of RC frame buildings using acceleration recordings for post-earthquake safety evaluations

机译:Unbiased rank selection for automatic hysteretic response extraction of RC frame buildings using acceleration recordings for post-earthquake safety evaluations

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摘要

A framework to extract a building's hysteretic response based on decomposing floor acceleration recordings into different ranks (i.e., decomposition levels) using the discrete wavelet transform (DWT) and then reconstructing signals considering only ranks which corresponded to the building's predominant response mode was recently proposed. However, current rank selection approaches require subjective judgment. This study looks to develop an unbiased rank selection method to enable automation of the hysteresis extraction framework for easier adoption in practice. The selection methodology was developed using response history analysis of several multistory reinforced concrete (RC) frame buildings and validated using data from large-scale shake-table tests performed at the E-Defense testing facility. It was observed that the proposed approach was able to accurately capture the response of RC frame buildings well in majority of cases. The exception to this was for significantly damaged buildings as the use of double integration of accelerations to estimate displacements and the DWT method meant that residual deformations were not well captured. However, significant strength degradation was still observed in the extracted hysteresis behavior for these cases, and thus these buildings would have been correctly classified as being heavily damaged. The step-by-step process to automatically select ranks is presented in the paper, and possible revenues for further improvement of the hysteretic response extraction framework are discussed.

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