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作者:

Zhong, Zilan (Zhong, Zilan.) | Ni, Bo (Ni, Bo.) | Shi, Yuebo (Shi, Yuebo.) | Shen, Jiaxu (Shen, Jiaxu.) | Du, Xiuli (Du, Xiuli.)

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EI Scopus SCIE

摘要:

Prior research demonstrated that the uncertainty of soil parameter can significantly impact the seismic response analysis and seismic performance evaluation of underground structures. To this end, the seismic response of a three-story, three-span subway station embedded in a typical layered engineering site was investigated in this study and the uncertainty of soil parameters was explicitly considered using the shear-wave velocity interlayer correlation model. In addition, an incremental dynamic analysis considering the uncertainty of the shear-wave velocity was performed using the one-dimensional convolutional neural network (1D-CNN) model as a surrogate model for the traditional finite element method. The results indicate that the uncertainty of soil shear-wave velocity increases the average PGA values for minor, moderate, and severe failures with a probability of exceeding 50% by about 10%, and increases the logarithmic standard deviation by about 40%. When considering the uncertainty of shear-wave velocity, the discreteness of the fragility curve will increase. Moreover, the rightward shift of the fragility curve midpoint leads to a comparatively more safety seismic performance evaluation of the structure under significant seismic input.

关键词:

1D-CNN Fragility analyses Subway station Uncertainty of soil parameter Shear-wave velocity

作者机构:

  • [ 1 ] [Zhong, Zilan]Beijing Univ Technol, Key Lab Urban Secur & Disaster Engn, Minist Educ, Beijing, Peoples R China
  • [ 2 ] [Ni, Bo]Beijing Univ Technol, Key Lab Urban Secur & Disaster Engn, Minist Educ, Beijing, Peoples R China
  • [ 3 ] [Shen, Jiaxu]Beijing Univ Technol, Key Lab Urban Secur & Disaster Engn, Minist Educ, Beijing, Peoples R China
  • [ 4 ] [Du, Xiuli]Beijing Univ Technol, Key Lab Urban Secur & Disaster Engn, Minist Educ, Beijing, Peoples R China
  • [ 5 ] [Shi, Yuebo]Beijing Glory PKPM Technol Co Ltd, China Acad Bldg Res, Beijing, Peoples R China

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来源 :

COMPUTERS AND GEOTECHNICS

ISSN: 0266-352X

年份: 2023

卷: 163

5 . 3 0 0

JCR@2022

ESI学科: COMPUTER SCIENCE;

ESI高被引阀值:19

被引次数:

WoS核心集被引频次: 20

SCOPUS被引频次: 20

ESI高被引论文在榜: 0 展开所有

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