Data-driven prediction for curved pipe jacking performance during underwater excavation of ancient shipwreck using an attention-based graph convolutional network approach

Abstract

Thrust is a crucial parameter to ensure the quality of underwater curved pipe jacking construction. A prediction framework based on hybrid deep learning is proposed to realize real-time prediction of thrust. The method contains graph convolutional neural network (GCN), gated recurrent unit (GRU), and self-attention (SA) mechanism. To enhance the interpretability of the method, Shapley Additive Explanations (SHAP), and causal discovery models are introduced within the framework. Taking the salvage project of the “Yangtze Estuary No. 2″ shipwreck as a case study, the prediction performance of the model is verified. The results show that the proposed model achieves excellent performance in thrust prediction for underwater curved pipe jacking, with an average MAE of 11.07 kN, RMSE of 13.96 kN, and R2 value of 0.934. The SHAP analysis reveals that angle, buried depth, and stratum changes exert significant influences on thrust. Furthermore, the spatial correlation and temporal correlation among features are revealed. Compared to other models, the proposed model exhibits superior prediction accuracy and is capable of handling various time step sizes, thereby providing valuable decision support for curved pipe jacking thrust prediction.