Prediction of Pile Running during Installation Using Deep Learning Method

Abstract

Pile running during the installation of offshore large diameter pipe piles poses a significant challenge to construction safety and pile bearing capacity. This paper proposes a deep learning (DL)-based method for predicting pile running occurrences. Utilizing a dataset of pile installation records collected from various construction sites, the DL model was trained and tested. The predictive capacity of the DL model was compared with conventional analytical methods, demonstrating its superior performance in terms of accuracy and robustness. Additionally, the SHAP (SHapley Additive exPlanations) method was employed for the sensitivity analysis of the model’s input variables, and the resultant importance ranking agreed well with the findings of existing studies, thus enhancing the reliability and interpretability of the model’s predictions.