A knowledge graph-guided multi-source data fusion method for risk assessment of water inrush in tunnel

Abstract

Water inrush are among the primary geological hazards encountered during tunnel construction. Therefore, it is essential to develop a scientific and effective risk assessment model for such hazards. However, existing methods suffer from poor generalizability, heavy reliance on subjective experience, and insufficient integration of multi-source data, making it difficult to comprehensively and accurately evaluate the risk of water and mud inrush. In this study, a novel risk assessment model for water inrush was proposed, which integrated knowledge graph-guided multi-source data fusion with machine learning. A water inrush case-based knowledge graph and knowledge graph embeddings were employed to achieve a unified representation of multi-source data. The embeddings extracted from the graph were used as features to construct risk assessment model. To validate the model, a comprehensive dataset of global water and mud inrush cases was collected to establish a karst tunnel disaster database. The model’s performance was evaluated using tenfold cross-validation and a separate test set, achieving an prediction accuracy of 97.67%. Furthermore, the model was validated in a real-world risk assessment for the Qiyueshan Tunnel, with results highly consistent with the on-site situation. The findings of this study provide valuable guidance for risk assessment of water inrush in tunnels.

Keywords

water inrush / risk assessment / knowledge graph embedding / multi-source data fusion

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