DOI QR코드

DOI QR Code

Probabilistic seismic demand assessment of self-centering concrete frames under mainshock-aftershock excitations

  • Song, Long L. (College of Civil and Transportation Engineering, Hohai University) ;
  • Guo, Tong (Key Laboratory of Concrete and Prestressed Concrete Structures of the Ministry of Education, Southeast University) ;
  • Shi, Xin (Key Laboratory of Concrete and Prestressed Concrete Structures of the Ministry of Education, Southeast University)
  • 투고 : 2018.08.20
  • 심사 : 2019.11.11
  • 발행 : 2019.12.10

초록

This paper investigates the effect of aftershocks on the seismic performance of self-centering (SC) prestressed concrete frames using the probabilistic seismic demand analysis methodology. For this purpose, a 4-story SC concrete frame and a conventional reinforced concrete (RC) frame are designed and numerically analyzed through nonlinear dynamic analyses based on a set of as-recorded mainshock-aftershock seismic sequences. The peak and residual story drifts are selected as the demand parameters. The probabilistic seismic demand models of the SC and RC frames are compared, and the SC frame is found to have less dispersion of peak and residual story drifts. The results of drift demand hazard analyses reveal that the SC frame experiences lower peak story drift hazards and significantly reduced residual story drift hazards than the RC frame when subjected to the mainshocks only or the mainshock-aftershock sequences, which demonstrates the advantages of the SC frame over the RC frame. For both the SC and RC frames, the influence of as-recorded aftershocks on the drift demand hazards is small. It is shown that artificial aftershocks can produce notably increased drift demand hazards of the RC frame, while the incremental effect of artificial aftershocks on the drift demand hazards of the SC frame is much smaller. It is also found that aftershock polarity does not influence the drift demand hazards of both the SC and RC frames.

키워드

과제정보

연구 과제 주관 기관 : National Natural Science Foundation of China, Natural Science Foundation of Jiangsu Province, China Postdoctoral Science Foundation

The support from: (1) the National Natural Science Foundation of China under Grants No. 51708172; (2) the Natural Science Foundation of Jiangsu Province under Grant BK20170890; and (3) the project funded by China Postdoctoral Science Foundation under Grant 2019M651674 is gratefully acknowledged.

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