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Convolutional neural network-based seismic fragility analysis of subway station structure considering spatial variation of site shear-wave velocity
被引:16
|作者:
Zhong, Zilan
[1
]
Ni, Bo
[1
]
Shi, Yuebo
[2
]
Shen, Jiaxu
[1
]
Du, Xiuli
[1
]
机构:
[1] Beijing Univ Technol, Key Lab Urban Secur & Disaster Engn, Minist Educ, Beijing, Peoples R China
[2] Beijing Glory PKPM Technol Co Ltd, China Acad Bldg Res, Beijing, Peoples R China
基金:
中国国家自然科学基金;
关键词:
Fragility analyses;
1D-CNN;
Uncertainty of soil parameter;
Shear-wave velocity;
Subway station;
FRAMEWORK;
D O I:
10.1016/j.compgeo.2023.105741
中图分类号:
TP39 [计算机的应用];
学科分类号:
081203 ;
0835 ;
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.
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页数:16
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