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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.
Highlights A Spatio-temporal Graph Convolutional Network for Clogging Detection was proposed. The tunneling graphs of complex networks were built from original time series data. More explanations were obtained by GCN combined with CN for clogging reasons in tunneling. The tunneling application shows potential value in learning from the explainable AI methods.
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.
Highlights A Spatio-temporal Graph Convolutional Network for Clogging Detection was proposed. The tunneling graphs of complex networks were built from original time series data. More explanations were obtained by GCN combined with CN for clogging reasons in tunneling. The tunneling application shows potential value in learning from the explainable AI methods.
Learning from explainable data-driven tunneling graphs: A spatio-temporal graph convolutional network for clogging detection
Gao, Yuyue (author) / Chen, Rui (author) / Qin, Wenbo (author) / Wei, Linchun (author) / Zhou, Cheng (author)
2023-01-02
Article (Journal)
Electronic Resource
English
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