Data cleaning and feature selection for gravelly soil liquefaction. Issue 145 (June 2021) - Hu, Jilei
Abstract: 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. Highlights: The DPT and Vs databases of the gravelly soil liquefaction are compiled and cleaned. A four-step approach is proposed to select the significant factors of gravelly soil liquefaction. The relationship between N120  and Vs  is refitted considering the correction of the depth of gravelly soil. An improved  CRR* - Vs1  curve that is considerably more accurate than the A&S model is proposed.