Prediction Model of Soils' Preconsolidation Pressure Based on Bayesian Ensemble Learning Algorithm

Accurate assessment of soils' preconsolidation stress (PS) is important in geotechnical engineering practice. In this paper it analyzes the influence of soils' preconsolidation stress, uses ensemble learning algorithms (XGBoost, RF) to capture the relationship between soil parameters and establishes prediction models. A Bayesian optimization method was used to determine the optimal parameters of the models, three machin elearning algorithms, namely SVR, KNN, and MLP, are introduced for comparison, and the models were statistically analyzed by three error metrics, including correlation coefficient(R²), root mean square error (RMSE) and mean absolute percentage error (MAPE). And finally, the prediction accuracy and generalization of each model were evaluated under 5-fold cross-validation (CV). The XGBoost-based prediction accuracy is the highest, with RMSE and MAPE of 20.80 kPa and 18.29%, respectively, followed by RF with 24.532 kPa and 19.15%, respectively. Meanwhile, in the case of PS as a regression variable, its characteristic importance is USS > VES > w > LL > PL. It shows that the ensemble learning algorithms (XGBoost, RF) are better than other algorithms in terms of prediction accuracy and generalization in the case of small-scale data sets, and can be used as an effective method for sensitivity analysis of geotechnical parameters.