The prediction methods for the collapsibility
coefficient of loess are predominantly based on regression analysis, but they
are significantly influenced by the regional characteristics of loess, leading
to insufficient prediction accuracy. Furthermore, existing machine learning
prediction methods have limitations in interpretability. Focusing on the loess
in the Longxi region, five pivotal influencing factors, namely, density,
saturation, self-weight stress, liquid limit, and internal friction angle, were
identified through correlation and cluster analyses. Subsequently, prediction
models for the loess collapsibility coefficient were constructed using extreme
gradient boosting (XGBoost), random forest (RF), support vector machine (SVM),
and regression analysis. Additionally, an interpretability analysis of the
prediction outcomes was performed using the Shapley additive explanations (SHAP)
technique. The results show that the correlation coefficients between the
collapsibility coefficient of Longxi loess and various indices such as density,
dry density, void ratio, saturation, and internal friction angle range from
0.63 to 0.90, indicating extremely strong or strong correlations. Both the
XGBoost and RF models demonstrate a prediction accuracy exceeding 90% for the
collapsibility coefficient and collapsibility grade, thereby outperforming the
SVM model and conventional regression methods. The SHAP analysis reveals that
loess with a density less than 1.44 g/cm3 has a higher probability
of collapsibility, while loess with a saturation greater than 29.0% has a
relatively lower collapsibility grade. Moreover, loess under high saturation
and high self-weight stress conditions exhibits weaker collapsibility tendency
and lower collapsibility grade. The findings provide a theoretical reference
for loess engineering construction in the Longxi region.