Assessment of seismic liquefaction potential based on Bayesian network constructed from domain knowledge and history data

Prediction of seismic liquefaction is difficult due to the uncertainties and complexity of multiple related factors. Bayesian network is a just right effective tool to deal the problem because of merging multiple source information and domain knowledge in a consistent system, reflecting and analyzing the interdependent uncertain relationships between variables. This paper used two ways to construct generic Bayesian network models with twelve significant factors of seismic liquefaction, of which the first model is constructed only by interpretive structural modeling and causal mapping approach for incomplete data contained huge missing values. Another one is constructed by combining K2 algorithm and domain knowledge for complete data. Compared with artificial neural network and support vector machine using 5-fold cross-validation, the two Bayesian network models provided a better performance, and the second Bayesian network model is slightly better than the first one. This paper also offers a sensitivity analysis of the input factors. In the twelve variables, standard penetration test number, soil type, vertical effective stress, depth of soil deposit, and peak ground acceleration have more significant influences on seismic liquefaction than others. Our results suggest that the Bayesian network is useful for prediction of seismic liquefaction and is simple to perform in practice.