Spatial Dynamic Early Warning of Different Positions in Underwater Tunnel Driven by Real-Time Monitoring Data

Abstract

Research on early warning of tunnel anomalies is fundamental for achieving intelligent management. However, most current methods for determining early-warning values of tunnel mechanics indicators are different to couple the nonlinear variation property and spatial positional difference. Therefore, this research presents a novel approach to tunnel early warning based on deep autoregressive learning method (DL-AR) that considers the spatiotemporal correlations of structural mechanics responses, specifically tailored to dynamically determine the warning thresholds at different spatial positions. The methodology introduces a framework for predictive modeling and instantiates it on a typical underwater shield tunnel. After thoroughly learning the temporal and spatial correlations of structural mechanical responses, accurate predictions are made for the evolving trends of structural behaviors and the probabilities to the reasonable fluctuation range. Based on these predictions, spatially varying alert thresholds for structural behaviors are proposed. To ensure the reliability of the proposed model, a series of discussions and validation experiments are conducted. Results indicate that the proposed model effectively captured the spatiotemporal characteristics of structural evolution and identified alert ranges, defining permissible variations in structural trends. The prediction results showed near to 99% consistency with actual data, a 5% enhancement compared to classical models. Any deviation beyond this range triggers an early warning, demonstrating the efficacy of model in anticipating and responding to potential structural issues.