Abstract
This paper proposes a probabilistic graphical model that integrates interpretive structural modeling (ISM) and Bayesian belief network (BBN) approaches to predict cone penetration test (CPT)-based soil liquefaction potential. In this study, an ISM approach was employed to identify relationships between influence factors, whereas BBN approach was used to describe the quantitative strength of their relationships using conditional and marginal probabilities. The proposed model combines major causes, such as soil, seismic and site conditions, of seismic soil liquefaction at once. To demonstrate the application of the propose framework, the paper elaborates on each phase of the BBN framework, which is then validated with historical empirical data. In context of the rate of successful prediction of liquefaction and non-liquefaction events, the proposed probabilistic graphical model is proven to be more effective, compared to logistic regression, support vector machine, random forest and naive Bayes methods. This research also interprets sensitivity analysis and the most probable explanation of seismic soil liquefaction appertaining to engineering perspective.
Highlights
Determination of soil liquefaction potential is a fundamental step for seismic-induced hazard mitigation
The benefits of Bayesian Belief Network (BBN) include the following compared to other methods: (1) BBN achieves a combination of qualitative and quantitative analysis; (2) BBN allows reversal inference and it is simple to obtain the ranking of factors affecting the casualties; (3) BBN has a good learning ability; (4) allows data to be combined with domain knowledge; and (5) Even with very limited sample sizes, BBN can demonstrate good prediction accuracy
The contributions of this paper are fourfold: (1) this article discusses the interdependence of different cone penetration test (CPT)-based seismic soil liquefaction variables, whereas the Bayesian Belief Network (BBN) approach uses conditional and marginal probabilities to describe the quantitative strength of their relationships; (2) the performance of the proposed model is comparatively assessed with four traditional seismic soil liquefaction modeling algorithms; (3) the sensitivity analysis of predictor variables is presented owing to know the effect of input factors on the liquefaction potential; and (4) the most probable explanation (MPE) of seismic soil liquefaction with reference to engineering perspective is presented
Summary
Determination of soil liquefaction potential is a fundamental step for seismic-induced hazard mitigation. The contributions of this paper are fourfold: (1) this article discusses the interdependence of different CPT-based seismic soil liquefaction variables, whereas the Bayesian Belief Network (BBN) approach uses conditional and marginal probabilities to describe the quantitative strength of their relationships; (2) the performance of the proposed model is comparatively assessed with four traditional seismic soil liquefaction modeling algorithms (logistic regression, SVM, RF, and Naïve Bayes); (3) the sensitivity analysis of predictor variables is presented owing to know the effect of input factors on the liquefaction potential; and (4) the most probable explanation (MPE) of seismic soil liquefaction with reference to engineering perspective is presented. In the last part, conclusions and future work are set out
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