Many studies have indicated that the triggering conditions for gravelly soil liquefaction are different from those for sandy soils. However, the existing prediction methods and models do not consider the differences. Moreover, most approaches of constructing Bayesian network (BN) models for predicting seismic liquefaction based on domain knowledge or data-driven are either too subjective or too objective, resulting in suboptimal structures. Therefore, to solve these shortcomings, two new BN models for predicting gravelly soil liquefaction are constructed using a new hybrid approach combining the maximal information coefficient and domain knowledge based on the dynamic penetration test and shear wave velocity test databases. The performance of the proposed hybrid approach is validated by comparing other existing modeling approaches, and two new BN models performed much better than other models in both two databases compared with the existing models or methods for predicting gravelly soil liquefaction in the training, validation, and testing sets. Furthermore, the differences and advantages of all methods or models mentioned in this paper are discussed, and factor sensitivity analysis in the BN models illustrates that those triggering conditions different from sandy liquefaction are worth considering in the prediction of gravelly soil liquefaction.

许多研究表明，砾石土液化的触发条件与砂土不同。然而，现有的预测方法和模型没有考虑差异。此外，基于领域知识或数据驱动构建贝叶斯网络（BN）模型以预测地震液化的大多数方法要么过于主观，要么过于客观，导致结构不理想。因此，为了解决这些缺点，基于动态渗透测试和剪切波速度测试数据库，使用结合最大信息系数和领域知识的新混合方法构建了两种用于预测砾石土壤液化的新BN模型。通过比较其他现有建模方法来验证所提出的混合方法的性能，与在训练、验证和测试集中预测砾石土壤液化的现有模型或方法相比，两个新的 BN 模型在两个数据库中的表现都比其他模型要好得多。此外，讨论了本文提到的所有方法或模型的差异和优点，BN模型中的因子敏感性分析表明，在砾石土液化预测中，与砂质液化不同的触发条件值得考虑。