Abstract: Based on the interpretive structural model and cause-sequence mapping approach, twelve representative factors, either qualitative or quantitative, of seismic liquefaction are selected to construct a Bayesian network (BN) model of seismic-induced liquefaction under the condition of a large number of incomplete data. Based on a set of incomplete data of the 2011 Pacific Coast liquefaction induced by Tohoku Earthquake, the performances of proposed model are assessed comprehensively with regard to the following five indexes: the overall accuracy, the area under the ROC curve, precision, the recall rate and F1 score, and then compared with a radial basis function (RBF) neural network model. It is shown that both the back evaluation and forward prediction of the BN model are better than those of the RBF neural network model, and the BN model also performs well for the case of incomplete data. In addition, the BN model is also suitable for predicting the liquefaction of different soils. Classification imbalance and sampling bias can influence the performances of the models significantly. Hence it is suggested that the five indexes mentioned above can be used to evaluate the performances of evaluation models.

Key words: seismic liquefaction, Bayesian network, interpretive structural model, causal mapping approach, probability prediction, evaluation index