Data cleaning and feature selection for gravelly soil liquefaction

摘要

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 Hn (the impermeable capping layer) larger than 0 m and Dn (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.