In:
PLOS ONE, Public Library of Science (PLoS), Vol. 16, No. 2 ( 2021-2-17), p. e0246387-
Abstract:
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.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0246387
DOI:
10.1371/journal.pone.0246387.g001
DOI:
10.1371/journal.pone.0246387.g002
DOI:
10.1371/journal.pone.0246387.g003
DOI:
10.1371/journal.pone.0246387.g004
DOI:
10.1371/journal.pone.0246387.g005
DOI:
10.1371/journal.pone.0246387.g006
DOI:
10.1371/journal.pone.0246387.g007
DOI:
10.1371/journal.pone.0246387.g008
DOI:
10.1371/journal.pone.0246387.g009
DOI:
10.1371/journal.pone.0246387.g010
DOI:
10.1371/journal.pone.0246387.g011
DOI:
10.1371/journal.pone.0246387.t001
DOI:
10.1371/journal.pone.0246387.t002
DOI:
10.1371/journal.pone.0246387.t003
DOI:
10.1371/journal.pone.0246387.t004
DOI:
10.1371/journal.pone.0246387.t005
DOI:
10.1371/journal.pone.0246387.t006
DOI:
10.1371/journal.pone.0246387.t007
DOI:
10.1371/journal.pone.0246387.s001
DOI:
10.1371/journal.pone.0246387.s002
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2021
detail.hit.zdb_id:
2267670-3
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