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.

Graph Neural Networks have emerged as an effective machine learning tool for multi-disciplinary tasks such as pharmaceutical molecule classification and chemical reaction prediction, because they can model non-euclidean relationships between different entities. Particle crushing, as a significant field of civil engineering, describes the breakage of granular materials caused by the breakage of particle fragment bonds under the modeling of numerical simulations, which motivates us to characterize the mechanical behaviors of particle crushing through the connectivity of particle fragments with Graph Neural Networks (GNNs). However, there lacks an open-source large-scale particle crushing dataset for research due to the expensive costs of laboratory tests or numerical simulations. Therefore, we firstly generate a dataset with 45,000 numerical simulations and 900 particle types to facilitate the research progress of machine learning for particle crushing. Secondly, we devise a hybrid framework based on GNNs to predict particle crushing strength in a particle fragment view with the advances of state of the art GNNs. Finally, we compare our hybrid framework against traditional machine learning methods and the plain MLP to verify its effectiveness. The usefulness of different features is further discussed through the gradient attribution explanation w.r.t the predictions. Our data and code are released at https://github.com/doujiang-zheng/GNN-For-Particle-Crushing.