Abstract:
Machine learning models are the viable option contrary to the conventional experimental
procedures that are complicated and tedious for liquefaction potential prediction. This study
leverages the strength of machine learning models including K-nearest neighbor (KNN), random
forest (RF), gradient boost (GB), extreme gradient boost (XGB), decision tree (DT) and artificial
neural network (ANN) for predicting the soil responses and liquefaction susceptibility by utilizing
the 500 SPT-N cases as input dataset. The predictive capabilities of models in assessing the
intricate relationship between various soils parameters are assessed by employing the evaluation
matrices of mean squared error (MSE), root mean square error (RMSE), mean absolute error
(MAE), Nash-Sutcliffe efficiency, Percent Bias, Weighted Index and R2. Moreover, the
comparative analysis of the models has been conducted to find the best optimized model for
liquefaction potential determination by accuracy matrix, Akaike information criterion (AIC) and
ranking analysis. The results indicated that RF model exhibited the highest prediction efficiency
for shear stress with R2 of 0.998. Similarly, GB indicated the better performance as compared to
other models in evaluating the shear wave velocity with 0.992 coefficient of determination (R2).
The most important and critical parameter for liquefaction maximum shear modulus (Gmax) is
predicted with high accuracy by all models with outperformance of ANN having R2 value of 0.999.
Moreover, liquefaction potential index (LPI) is also well predicted by XGBoost model. The
performance trend of all the models for each of the parameters is also in accordance with AIC
criteria. Furthermore, scaling analysis of all models for collective comparison put RF model on
rank first for overall prediction of liquefaction potential and dynamic properties. XGBoost and DT
showed second and third best performance, followed by GB as fourth, ANN as fifth and KNN as
least effective model for this task. The findings of this study offer sophisticated models for
evaluating soil behavior and liquefaction potential, which has important ramifications for
geotechnical engineering.
