A lightweight physics-data-driven method for real-time prediction of subgrade settlements induced by shield tunneling

Real-time prediction of subgrade settlement caused by shield tunneling is crucial in engineering applications. However, data-driven methods are prone to overfitting, while physical methods rely on certain assumptions, making it difficult to select satisfactory parameters. Although there are currently physics-data-driven methods, they typically require extensive iterative calculations with physical models, which makes them unavailable for real-time prediction. This paper introduces a lightweight physics-data-driven method for predicting subgrade settlement caused by shield tunneling. The core concept involves using a single calculation of the physical model to provide a weak constraint. A deep learning network is then designed to capture spatiotemporal correlations based on ConvLSTM. By iteratively incorporating real-time data, the learning of physical constraints is further enhanced. This method combines the predictive power of data-driven method with the reasonable constraints of physical laws, validated a good performance in a practical project. The results demonstrate that this method meets real-time prediction requirements in engineering, achieving an coefficient of determination of 0.980, a root mean square error of 0.22 mm, and a mean absolute error of 0.15 mm. Furthermore, it outperforms both physical and data-driven models and demonstrates good generalization performance. This study provides effective guidance for engineering practices.