Prediction of Vertical Bearing Capacity of Driven Pile Based on Cone Penetration Test and Machine Learning

Abstract:

The main factors influencing the bearing capacity of a single pile were analyzed， the cone penetration test data were preprocessed using the dimensionality reduction algorithm， the main features were screened using the importance analysis function of tree model， and the hyperparameters were determined using the Bayesian optimization algorithm. A prediction model of vertical bearing capacity of driven pile was proposed based on the machine learning algorithm. The validity and robustness of the prediction model were evaluated using accuracy test and Monte Carlo simulation test， and compared with the empirical models. The results show that the support vector machine （SVM） model， the random forest （RF） model， and the extreme gradient boosting （XGBoost） model can accurately predict the vertical bearing capacity of driven piles， whose accuracy is significantly higher than that of the empirical relationship model. The comprehensive analysis indicates that the XGBoost model should be selected to predict the vertical bearing capacity of driven piles.