The unprecedented liquefaction-related land damage during earthquakes has highlighted the need to develop a model that better interprets the liquefaction land damage vulnerability (LLDV) when determining whether liquefaction is likely to cause damage at the ground’s surface. This paper presents the development of a novel comprehensive framework based on select case history records of cone penetration tests using a Bayesian belief network (BBN) methodology to assess seismic soil liquefaction and liquefaction land damage potentials in one model. The BBN-based LLDV model is developed by integrating multi-related factors of seismic soil liquefaction and its induced hazards using a machine learning (ML) algorithm-K2 and domain knowledge (DK) data fusion methodology. Compared with the C4.5 decision tree-J48 model, naive Bayesian (NB) classifier, and BBN-K2 ML prediction methods in terms of overall accuracy and the Cohen’s kappa coefficient, the proposed BBN K2 and DK model has a better performance and provides a substitutive novel LLDV framework for characterizing the vulnerability of land to liquefaction-induced damage. The proposed model not only predicts quantitatively the seismic soil liquefaction potential and its ground damage potential probability but can also identify the main reasons and fault-finding state combinations, and the results are likely to assist in decisions on seismic risk mitigation measures for sustainable development. The proposed model is simple to perform in practice and provides a step toward a more sophisticated liquefaction risk assessment modeling. This study also interprets the BBN model sensitivity analysis and most probable explanation of seismic soil liquefied sites based on an engineering point of view.

地震期间史无前例的与液化相关的土地破坏凸显了在确定液化是否可能对地面造成破坏时，需要开发一种能够更好地解释液化土地破坏脆弱性（LLDV）的模型的需求。本文介绍了一种新颖的综合框架的开发，该框架基于贝叶斯渗透测试的精选案例历史记录，使用贝叶斯信念网络（BBN）方法来评估一个模型中的地震土液化和液化土地破坏潜力。基于BBN的LLDV模型是通过使用机器学习（ML）算法-K2和领域知识（DK）数据融合方法整合地震土液化的多个相关因素及其诱发危害而开发的。与C4.5决策树-J48模型相比，朴素贝叶斯（NB）分类器 BBN K2和DK模型在整体精度和Cohen卡伯系数方面具有较好的性能，并提供了一种替代性的LLDV框架来表征土地对液化引起的破坏的脆弱性。提出的模型不仅定量预测了地震土液化潜力及其地面破坏的可能性，而且可以识别出主要原因和断层发现状态组合，其结果可能有助于为可持续发展制定减轻地震风险的措施。所提出的模型在实践中易于执行，并为迈向更复杂的液化风险评估模型迈出了一步。