Liquefaction-induced lateral displacement is responsible for considerable damage to engineered structures during major earthquakes. Therefore, an accurate estimation of lateral displacement in liquefaction-prone regions is an essential task for geotechnical experts for sustainable development. This paper presents a novel probabilistic framework for evaluating liquefaction-induced lateral displacement using the Bayesian belief network (BBN) approach based on an interpretive structural modeling technique. The BBN models are trained and tested using a wide-range case-history records database. The two BBN models are proposed to predict lateral displacements for free-face and sloping ground conditions. The predictive performance results of the proposed BBN models are compared with those of frequently used multiple linear regression and genetic programming models. The results reveal that the BBN models are able to learn complex relationships between lateral displacement and its influencing factors as cause-effect relationships, with reasonable precision. This study also presents a sensitivity analysis to evaluate the impacts of input factors on the lateral displacement.

在大地震中，液化引起的横向位移会对工程结构造成相当大的破坏。因此，对易发生液化的区域进行横向位移的准确估算是岩土工程专家实现可持续发展的一项基本任务。本文提出了一种新的概率框架，该框架使用基于解释性结构建模技术的贝叶斯信念网络（BBN）方法评估液化引起的侧向位移。BBN模型是使用广泛的病历记录数据库进行训练和测试的。提出了两个BBN模型来预测自由面和倾斜地面条件的横向位移。所提出的BBN模型的预测性能结果与经常使用的多元线性回归和遗传规划模型的预测性能结果进行了比较。结果表明，BBN模型能够以合理的精度学习横向位移与其影响因素之间的复杂关系，并将其作为因果关系。这项研究还提出了敏感性分析，以评估输入因素对侧向位移的影响。