Learning from explainable data-driven tunneling graphs: A spatio-temporal graph convolutional network for clogging detection

Abstract

Clogging challenges caused by mud cake in shield tunneling can hinder the efficiency, making it of great significance for clogging detection during tunneling. However, low explainability of previous intelligent computing methods block their applications. An explainable spatiotemporal graph convolutional network for clogging detection is therefore proposed to judge the risk level of clogging for each ring. The graphs are transformed from original multi-dimensional time-series monitoring data, which include spatio-temporal information, whose risk are leveled by experts as labels. A practical tunneling case is applied to discuss the results of the model and the data from another tunneling line is applied to validate the robustness. The hierarchical pooling process and attention mechanism highlight the important nodes in each graph as the spatio-temporal explainability. Typical clogging characteristics can be observed in those important nodes. With complex network indices, more detailed explanations are obtained, showing potential value in learning from the explainable AI.