Determination for dynamic pre-warning range of different positions in river tunnels using a deep learning method

Monitoring and early warning systems are essential for ensuring the long-term stability of tunnels. However, existing methods often overlook the impact of multiple external factors, such as water pressure and temperature, on tunnel structural behavior, limiting their intelligence and accuracy. To address this gap, we propose a Dynamic Pre-Warning model (DPWNet), constructed as a Deep Probabilistic Autoregressive Architecture, which integrates deep learning to predict structural responses by considering spatiotemporal correlations and dynamic external loads. DPWNet incorporates Markov Chain Monte Carlo sampling during the decoding phase to simulate a range of potential working conditions, accounting for the complex interactions between environmental and structural factors. This probabilistic framework allows real-time adaptive adjustment of pre-warning thresholds based on scenario likelihoods. DPWNet is applied to an underwater shield tunnel, demonstrating significant improvements in accuracy. Specifically, the model calculates the 90% confidence interval of structural responses under multiple factors, translating probabilistic forecasts into actionable thresholds. Experimental results show that DPWNet reduces mean absolute error by 42.0%, root mean square error by 29.2%, and improves Pearson correlation coefficient by 1.3% over 30 days compared to existing methods. These results highlight the model’s reliability and its potential to advance the intelligent monitoring of underwater shield tunnels.