Prediction of the displacements of the pile tops and ground surface around piles based on machine learning algorithms.

The soil squeezing effect of pile groups may cause displacements and deformation at the pile tops and ground surface around piles. In severe cases, it can cause problems such as broken piles, cracking of adjacent buildings or cracking of pipes. Artificial intelligence provides a new way to predict horizontal displacements of the pile tops and ground surface around piles caused by soil squeezing effect. The adaptive boosting (AdaBoost) algorithm was applied to the back propagation (BP) neural network model to form the Adaboost-BP model, which improved the learning ability of the BP neural network. For small sample datasets, the prediction accuracy of AdaBoost-BP model, Random Forest (RF) model and Deep Neural Networks (DNN) model is higher than that of BP model. For large sample datasets, the prediction accuracy of various models has improved, but the BP model is lower than that of other models. Analysis shows that the horizontal distance and angle between the center of the bearing platform and the center of the pile tops (or ground surface monitoring points) are the two most important influencing factors. The resting time is also an important influencing factor. Moisture content, relative density, and internal friction angle have a more significant influence on the horizontal displacements of the pile tops and ground surface around piles than other soil property indexes. Quantile regression analysis shows that the horizontal displacements is negatively correlated with the horizontal distance, and positively correlated with the rest time and moisture content. The prediction accuracy of machine learning algorithms (such as DNN) is higher than that of the cylindrical hole expansion method.