Risk Prediction of Tunnel Water and Mud Inrush Based on Decision-Level Fusion of Multisource Data

This paper addresses the accuracy and timeliness limitations of traditional comprehensive prediction methods by proposing an approach of decision-level fusion of multisource data. A risk prediction indicator system was established for water and mud inrush in tunnels by analyzing advanced prediction data for specific tunnel segments. Additionally, the indicator weights were determined using the analytic hierarchy process combined with the Huber weighting method. Subsequently, a multisource data decision-layer fusion algorithm was utilized to generate fused imaging results for tunnel water and mud inrush risk predictions. Meanwhile, risk analysis was performed for different tunnel sections to achieve spatial and temporal complementarity within the indicator system and optimize redundant information. Finally, model feasibility was validated using the CZ Project Sejila Mountain Tunnel segment as a case study, yielding favorable risk prediction results and enabling efficient information fusion and support for construction decision-making.