This study examines the details of creating and validating an empirical liquefaction model, using a worldwide cone penetration test (CPT) liquefaction database with the intent of incorporating the rigor found in predictive modeling in other fields and addressing shortcomings of existing models. Our study implements a logistic regression within a Bayesian measurement error framework to incorporate uncertainty in predictor variables and allow for a probabilistic interpretation of model parameters when making future predictions. The model is built using a hierarchal approach to account for intra-event correlation in loading variables and differences in event sample sizes. The model is tested using an independent set of recent case histories.

We found that the Bayesian measurement error model considering two predictor variables, normalized CPT tip resistance and cyclic stress ratio decreased model uncertainty while maintaining predictive utility for new data. Hierarchical models revealed high model uncertainty potentially due to the database lacking in high loading non-liquefaction sites. Models considering friction ratio as a predictor variable performed worse than the two variable case and will require more data or informative priors to be adequately estimated. The framework developed is flexible and can be extended using different methods of predictor variable selection, model function forms, and validation processes.

这项研究使用全球锥形渗透测试（CPT）液化数据库检查了创建和验证经验性液化模型的细节，目的是结合其他领域预测模型中发现的严谨性并解决现有模型的不足。我们的研究在贝叶斯测量误差框架内实现了逻辑回归，以将不确定性纳入预测变量中，并允许在进行未来预测时对模型参数进行概率解释。该模型是使用分层方法构建的，以考虑加载变量和事件样本大小差异中的事件内相关性。使用一组独立的近期案例历史记录对模型进行测试。

我们发现，考虑两个预测变量，标准化的CPT尖端电阻和循环应力比的贝叶斯测量误差模型减少了模型不确定性，同时保持了对新数据的预测效用。分层模型显示出较高的模型不确定性，这可能是由于数据库缺乏高负荷的非液化站点所致。将摩擦比作为预测变量的模型比两个变量的情况更糟，并且需要更多数据或先验信息进行充分估计。开发的框架非常灵活，可以使用预测变量选择，模型函数形式和验证过程的不同方法进行扩展。