Rapid Evaluation Method to Vertical Bearing Capacity of Pile Group Foundation Based on Machine Learning.

With the continuous increase in bridge lifespans, the rapid check and evaluation of the vertical bearing capacity for the pile foundations of existing bridges have been in greater demand. The usual practice is to carry out compression bearing tests under static loads in order to obtain the accurate ratio of the dynamic to static stiffness. However, it is difficult and costly to conduct in situ experiments for each pile foundation. Herein, a rapid evaluation method to measure the vertical bearing capacity of bridge pile foundations is proposed. Firstly, a 3D-bearing cap-pile group-soil interaction model was established to simulate a bearing test of a pile foundation that was subject to static loads and dynamic loads, and then the numerical results were validated by in situ dynamic and static loading tests on an abandoned bridge pier with the same pile group foundation; the dataset for machine learning was constructed using the numerical results, and finally, the bearing capacity of the pile foundation could be predicted rapidly. The results show the following outcomes: the established numerical model can effectively simulate dynamic and static loading tests of pile foundations; the intelligent prediction model based on machine learning can predict the ratio of static stiffness to dynamic stiffness and can thus rapidly evaluate the vertical residual bearing capacity and the designed ultimate loading capacity, allowing for the nondestructive testing and evaluation of the pile foundations of existing bridges.