Prediction of Ultimate Axial Load-carrying Capacity for Driven Piles using Machine Learning Methods

摘要

Determination of pile drivability is one of the challenging tasks in the design and construction. Especially it is utilized in complex geological environment. In this research, a promising practical machine learning tool known as eXtreme Gradient Boosting (XGBoost) model is applied for prediction of pile bearing capacity. Maximum Compressive Stress (MCS), Maximum Tensile Stress (MTS) and Blow Per Foot (BPF) are the key targets. Moreover, the other two methods. Backpropagation Neural Network (BPNN) and Random Forest (RF) are developed for comparison purposes. A pile database more than four thousand data sets are used into generate training and testing samples. The validation and comparison of the three regression models are evaluated by several performance indices-the root mean square error (RMSE), the mean absolute error (MAE) and the coefficient of determination (R²). The results show that XGBoost algorithm has higher prediction accuracy and stability than the rest two methods for solving complicated and nonlinear problems among pile, hammer, soil, construction technology and pile drivability. It is concluded that XGBoost as a reliable and accurate technique could potentially be further employed for target variables estimation and provide significant references for other similar pile engineering.