Probabilistic evaluation of earthquake-induced liquefaction using Bayesian network based on a side-by-side SPT-CPT database

In situ tests such as standard penetration test (SPT) and cone penetration test (CPT) are often conducted to evaluate the probability of earthquake-induced liquefaction. However, the models adopted have seldom attempted to utilize SPT and CPT data simultaneously. In this study, a side-by-side SPT-CPT database at historical earthquake sites is established; then, a Bayesian network model is constructed to predict the probability of soil liquefaction based on this database, with which the SPT and CPT data are utilized simultaneously. Next, comparative studies are undertaken to illustrate the superiority of the Bayesian network-based probabilistic soil liquefaction model developed over other models, in terms of six SPT- and CPT-based conventional liquefaction models in the literature and two Bayesian network-based models. It should be noted that the liquefaction sites with two in situ tests are scarce and side-by-side SPT-CPT data can be incomplete, which leads to challenges in applying the Bayesian network model developed. To address this problem, correlations between SPT and CPT data are analyzed, and these correlations are further included in the Bayesian network model; as a result, a modified Bayesian network model is reached. Finally, the influence of the proportion of missing data in the incomplete SPT-CPT data on the liquefaction prediction accuracy is discussed.