Depth-consistent models for probabilistic liquefaction potential assessment based on shear wave velocity

被引:0
|
作者
Tianpeng Wang
Shihao Xiao
Jie Zhang
Baocheng Zuo
机构
[1] Tongji University,Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education and Department of Geotechnical Engineering
[2] The Hong Kong University of Science and Technology,Department of Civil and Environmental Engineering
[3] China Energy Engineering Group Guangdong Electric Power Design Institute Co.,undefined
[4] Ltd,undefined
来源
Bulletin of Engineering Geology and the Environment | 2022年 / 81卷
关键词
Depth consistency; Probabilistic assessment; Soil liquefaction; Shear wave velocity; Measurement uncertainty;
D O I
暂无
中图分类号
学科分类号
摘要
In the Chinese code models for seismic design, the shear wave velocity (Vs)-based model for liquefaction potential assessment is built based on the depth consistency assumption, i.e., the seismic demand is a non-decreasing function of the soil depth. It provides an alternative way to construct empirical models based on past case histories. However, the current Vs-based Chinese code model is deterministic in nature. In this paper, the depth consistency assumption for Vs data is validated with models built within the cyclic stress ratio (CSR) framework. Then, two Vs-based probabilistic models for liquefaction potential assessment are built based on the depth consistency assumption, i.e., a model with explicit consideration of the measurement uncertainty and a model without explicit consideration of the measurement uncertainty. It is found that the performances of the two suggested models are comparable with those of two frequently used models built within the CSR framework and are superior to the Chinese code model. For a site where the measurement uncertainty is not available, the model without explicit consideration of the measurement uncertainty should be used. For a site where measurement uncertainty is available, it is suggested that the model with explicit consideration of the measurement uncertainty should be used as it is more conservative.
引用
收藏
相关论文
empty
未找到相关数据