Graph-convolutional neural networks for predicting tunnel boring machine performance

Abstract

Accurately predicting Tunnel Boring Machine (TBM) performance is critical in construction processes. Traditional machine learning models often struggle to achieve accurate prediction as they fail to capture both the temporal dependencies and the intricate interactions among operational features (e.g., torque, thrust), which are essential for accurate prediction of TBM performance. This paper proposes Graph-ConvNet, a new deep learning architecture that combines Graph Neural Networks (GNNs) and Convolutional Neural Networks (CNNs) to capture both temporal dependencies and feature interactions. TBM data is represented as a temporal graph, where each node corresponds to a time step and edges capture temporal dependencies between them. A Graph Neural Network (GNN) models this structure, while CNNs are applied within each node to extract feature interactions, enhancing the overall representation. Experiments on real-world TBM data demonstrate that Graph-ConvNet significantly improves prediction accuracy and robustness compared to conventional methods.