A New Shear Wave Velocity-Based Liquefaction Probability Model Using Logistic Regression: Emphasizing Fines Content Optimization

Abstract

A new liquefaction probability model based on shear wave velocity (Vs) was developed through a detailed comparative analysis of existing evaluation methods. Publicly available shear wave velocity liquefaction data were used to evaluate multiple existing liquefaction probability assessment methods under various probability contours and fines content levels. Significant performance differences were observed among the formulae under varying fines content levels. To construct the new model, the random forest feature importance ranking algorithm was employed to select the key parameters, including the effective stress-normalized shear wave velocity (Vs1), corrected cyclic resistance ratio (CSR7.5), magnitude (MW), depth (Z), and fines content (FC). Using these parameters, a new liquefaction probability assessment formula was developed utilizing the logistic regression model to predict the liquefaction probability. The new formula’s performance was subsequently evaluated through a detailed case analysis and validation. The results demonstrate that the new formula achieves a higher accuracy (3–11%) for the liquefaction assessment compared to the existing formulae, performing consistently well across different probability contours and fines content levels, especially in areas with high fines content. This study provides theoretical support and empirical evidence for optimizing the shear wave velocity-based liquefaction probability assessment methods.