Conventional methods for liquefaction discrimination are often semi-empirical

prone to human factors

with low success rates and balance. Moreover

current machine learning approaches lack ample sample support and have specific limitations. Through the integration of liquefaction datasets

11 features were chosen: corrected standard penetration test blow count

fine content

soil layer depth

groundwater table depth

total overburden stress

effective overburden stress

threshold acceleration

cyclic shear stress ratio

shear wave velocity

earthquake magnitude

and peak ground acceleration. A convolutional neural network (CNN) model was established. The Borderline SMOTE technique was introduced to address the issue of imbalanced datasets. The CNN model was compared against random forest

logistic regression

support vector machine

extreme gradient boosting models

and methods specified in Chinese codes. Furthermore

the SHapley Additive exPlanations (SHAP) algorithm was utilized to examine the influence trends of input features on the prediction outcomes. The results demonstrated that the CNN model attained an accuracy of 92.58%

outperforming all metrics of the four machine learning models and the method specified in Chinese codes. Examination of the SHAP results unveiled that soil layers with corrected standard penetration blow numbers below 15 exhibited a higher liquefaction probability

whereas layers with cyclic stress ratios under 0.25 were less prone to liquefaction. The influence patterns of each factor align with current understanding

indicating the prediction model’s credibility and reliability.