A continuous Bayesian network regression model for estimating seismic liquefaction‐induced settlement of the free‐field ground

Abstract The Bayesian network (BN) method has been successfully applied to evaluate earthquake liquefaction‐induced settlement of free‐field ground in recent years due to several specific advantages. However, the existing BN models need to discretize all continuous variables, and therefore, they can only predict the range value of the settlement (i.e., classification). Thus, information loss is inevitable in the process of discretization, which will largely reduce the prediction accuracy of the model. To realize the application of the BN method in the regression prediction of seismic liquefaction‐induced settlement, this study proposes a hybrid modelling method combining a hill‐climbing algorithm and domain knowledge to construct the structure of a continuous BN regression model based on historical liquefaction‐induced settlement data, and then the conditional linear Gaussian approach is used to learn the conditional probability distributions of parameters. A five‐fold cross‐validation test is used to demonstrate better generalization performance and advantages (such as considering model uncertainty and prior knowledge) of the continuous BN regression model compared with a discrete BN classification model, simplified methods such as the Tokimatsu & Seed and Ishihara & Yoshimine methods, and an artificial neural network model. Their advantages and disadvantages are discussed. In addition, the other two continuous BN models using the Arias intensity and cumulative absolute velocity instead of the peak ground acceleration perform slightly worse than the proposed continuous BN model, and the reason for this difference is discussed.