Abstract:
This paper presents the application of Gaussian Process Regression (GPR) and M5 Model Tree as two alternative
data driven modeling practices for prediction of soil liquefaction. The initial effective mean confining pressure
(σ’mean), initial relative density after consolidation (Dr), percentage of fines content (FC); uniformity coefficient
(Cu); Coefficient of curvature (Cc), mean grain size (D50) etc. are used as model inputs to predict strain energy
density (W) required for triggering the liquefaction. The performance evaluation criteria like Mean Absolute Relative
Error (MARE), Coefficient of Correlation (R), Root Mean Square Error (RMSE) for the validation datasets are found to
be 6.381, 0.849 0.266 respectively. Use of multiple statistical criteria and graphical plots confirmed the superiority of
PuK Kernel based GPR model over five different empirical models, two Linear Genetic Programming (LGP) based
expressions, Artificial Neural Network (ANN) and M5Model tree based predictions. Further, a parametric sensitivity
analysis performed on input parameters showed that σ′mean is the most influencing predictor to explain the variations
of the capacity energy than other input parameters.
