A Novel IoT-Based Spatiotemporal Prediction and Resilience Optimization Method for Tunnel-Induced Ground Settlement

With the acceleration of urbanisation, the problem of ground settlement in tunnel construction has become a serious challenge. Aiming at the existing ground settlement monitoring and resilience management problems such as limited monitoring range, neglected spatio-temporal characteristics, and poor combination of prediction results and resilience management, this paper proposes a two-stage spatio-temporal prediction and resilience optimization for tunnel-induced ground settlement. Specifically, firstly, this paper constructs a comprehensive monitoring architecture integrating SBAS-InSAR technology and Internet of Things (IoT) technology, which realises accurate and comprehensive monitoring of ground settlement. Secondly, a two-stage spatio-temporal prediction method of ground settlement is proposed: based on the wide-area spatio-temporal data acquired by SBAS-InSAR technology, the spatio-temporal transformer model is used to make the preliminary prediction. Then, with the small-area variables collected by IoT, the parameter-seeking optimisation algorithm based on the Grid Search-Particle Swarm is used in conjunction with the Time Convolutional-Bidirectional Long and Short-Term Memory Network (GR-PSO-TCN-BiLSTM) model to correct the prediction error. In terms of resilience optimisation, this paper proposes a multi-stage resilience enhancement strategy based on ground settlement prediction, which combines prevention importance, degradation importance and recovery importance, aiming to maximise the resilience of tunnel-induced ground settlement area. Finally, an empirical analysis using the traffic along the Zhengzhou Metro as an example verifies the effectiveness of the proposed method. These results indicate that coupling wide-area remote sensing with local IoT correction can substantially improve settlement prediction accuracy and provide actionable guidance for maintenance prioritization, thereby enhancing the robustness and recovery capability of metro systems.