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论文类型：期刊论文

发表时间：2022-06-29

发表刊物：振动与冲击

所属单位：建设工程学部

卷号：37

期号：18

页面范围：177-183

ISSN号：1000-3835

摘要：Based on the Bayesian network method, a Bayesian network model for assessing seismic liquefaction-induced settlement was constructed, in which 12 significant factors including earthquake parameters, soil parameters and field conditions combining with the liquefaction potential and liquefaction potential index were considered. Through some cases study, it is shown the Bayesian network model has obvious advantages in the assessment performance, comparing with the RBF (Radial Basis Function) neural network method and I & Y (Ishihara & Yoshimine) simplified calculation method. The Bayesian network model not only has better assessment accuracy and reliability, but can also perform reverse causal reasoning. In the analysis of sensitive factors to the two machine learning models, the ground peak acceleration, duration of earthquake and standard penetration test blow count are more sensitive among the 12 factors, which are the same as those considered in the I & Y simplified calculation method. © 2018, Editorial Office of Journal of Vibration and Shock. All right reserved.

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