Liquefaction of gravelly soil has been reported for several historical earthquakes. However, the data size remains insufficient for guaranteeing a high-performance prediction model, especially because the data quality used for the model building has not been evaluated in previous studies. In addition, the significant factors used to construct a gravelly soil liquefaction model remain unclear. To overcome these issues, the following key efforts are made in this study: (1) significantly expanded databases are accumulated for filed performance case histories obtained using dynamic penetration and shear wave velocity tests; (2) the data quality is improved by screening, correction, and repair of filed data case histories; (3) a framework is proposed to identify significant factors for gravelly soil liquefaction; and (4) the thresholds for two triggers of gravelly soil liquefaction are updated as H_n (the impermeable capping layer) larger than 0 m and D_n (the thickness of the unsaturated zone between the groundwater table and the capping layer) less than or equal to 4 m. Data cleaning and identification of significant factors can both improve the predictive performance of a model.

曾有几次历史性地震报道了砾石土的液化。但是，数据大小仍然不足以保证高性能的预测模型，特别是因为用于模型构建的数据质量尚未在先前的研究中进行评估。此外，用于构造砾石土液化模型的重要因素仍不清楚。为了克服这些问题，本研究做出了以下主要努力：（1）积累了大幅度扩展的数据库，用于通过动态穿透和横波速度测试获得的性能案例历史记录；（2）通过筛选，更正和修复归档的数据案例历史记录来提高数据质量；（3）提出了一个框架，以识别砾石土液化的重要因素；H _n（不可渗透的覆盖层）大于0 m，D _n（地下水位和覆盖层之间的不饱和区域的厚度）小于或等于4 m。数据清理和重要因素的识别都可以提高模型的预测性能。