Key factors influencing earthquake-induced liquefaction and their direct and mediation effects

被引:10
作者
Hu, Jilei [1 ]
Tan, Yunzhi [1 ]
Zou, Wenjun [2 ]
机构
[1] China Three Gorges Univ, Coll Civil Engn & Architecture, Yichang, Hubei, Peoples R China
[2] China Three Gorges Univ, Med Coll, Yichang, Hubei, Peoples R China
来源
PLOS ONE | 2021年 / 16卷 / 02期
基金
中国国家自然科学基金;
关键词
FIT INDEXES;
D O I
10.1371/journal.pone.0246387
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Many factors impact earthquake-induced liquefaction, and there are complex interactions between them. Therefore, rationally identifying the key factors and clarifying their direct and indirect effects on liquefaction help to reduce the complexity of the predictive model and improve its predictive performance. This information can also help researchers understand the liquefaction phenomenon more clearly. In this paper, based on a shear wave velocity (V-s) database, 12 key factors are quantitatively identified using a correlation analysis and the maximum information coefficient (MIC) method. Subsequently, the regression method combined with the MIC method is used to construct a multiple causal path model without any assumptions based on the key factors for clarifying their direct and mediation effects on liquefaction. The results show that earthquake parameters produce more important influences on the occurrence of liquefaction than soil properties and site conditions, whereas deposit type, soil type, and deposit age produce relatively small impacts on liquefaction. In the multiple causal path model, the influence path of each factor on liquefaction becomes very clear. Among the key factors, in addition to the duration of the earthquake and V-s, other factors possess multiple mediation paths that affect liquefaction; the thickness of the critical layer and thickness of the unsaturated zone between the groundwater table and capping layer are two indirect-only mediators, and the fines content and thickness of the impermeable capping layer induce suppressive effects on liquefaction. In addition, the constructed causal model can provide a logistic regression model and a structure of the Bayesian network for predicting liquefaction. Five-fold cross-validation is used to compare and verify their predictive performances.
引用
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页数:24
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