Bayesian network models for probabilistic evaluation of earthquake-induced liquefaction based on CPT and Vs databases

Cone penetration test (CPT) and shear wave velocity (V-s) based databases have been used for the evaluation of earthquake-induced soil liquefaction, but probabilistic evaluation of soil liquefaction using Bayesian network methods has seldom been attempted using CPT and V-s results. In this study, these databases are first used to construct two new Bayesian network (BN) models for predicting the probability of the occurrence of soil liquefaction and then compared with four simplified procedures and a Bayes classifier for soil liquefaction evaluation. The present study shows that the two new BN models are preferred over the simplified procedures and the Bayes classifier. The reasons for the better performance and advantages of the BN models are discussed. In addition, a converging BN model combing CPT, SPT (standard penetration test), and V-s databases is simultaneously attempted to further improve the prediction performance and applicability.