A Novel Subway Tunnel Collapse Risk Prediction Model using Mutation Theory

With the rapid expansion of urbanization, subway tunnel construction has grown significantly in scale and speed, but frequent collapse incidents pose severe risks to safety, economy, and public infrastructure.This study proposes a novel risk prediction model for subway tunnel collapse using mutation theory, which effectively captures the dynamic and discontinuous nature of collapse events.The model integrates geological, hydrological, and construction parameters and employs real-time monitoring data to achieve dynamic risk classification.Numerical simulations indicate a strong correlation between settlement, clearance convergence, and deformation, with the top settlement reaching 0.28 mm and plastic strain increasing from 1.3 10 -4 to 1.23 10 -3 after excavation.Case verification reveals high collapse risks at multiple monitoring points, underscoring the importance of robust monitoring and preventive strategies.The findings demonstrate that mutation theory-based risk modeling is feasible and reliable, providing a new theoretical framework and practical tool for early warning and risk management in subway tunnel construction.Future research will explore the model's applicability to diverse geological conditions and its integration with advanced monitoring systems to enhance prediction accuracy and risk mitigation.