Diagnosis method for the structural uplift of a water-rich shield tunnel based on the spatiotemporal characteristics of the densely distributed strain data

Shield tunnels in water-rich strata are usually large in scale, long in length, and located in complex operating environments. The diagnostic results for tunnel structural uplift are easily affected by complex environmental factors. How to extract the key information related to the structural uplift of the tunnels from the massive monitoring data of distributed optical fiber sensors in this complex water-rich environment and accurately diagnose the structural uplift of the tunnels remains a difficult problem that urgently needs to be solved. This paper proposes a diagnostic approach for structural uplift in water-rich shield tunnels, which utilizes the spatiotemporal features of the densely distributed strain data to address this challenge. On the one hand, the spatial interdependence of the densely distributed strain data is analyzed. By combining k-means clustering with the artificial bee colony algorithm and referring to the distribution characteristics of the tunnel surrounding rock, a clustering algorithm for the dense strain measurements along the length direction of the tunnel is proposed. Then, a spatial interdependence model for the densely distributed measurements is established based on a one-dimensional convolutional neural network. On the other hand, the spatial interdependence features of the strain data are analyzed in the time domain. The factors influencing the spatial interdependence residuals of the strain data within each category are analyzed by using the principal component analysis algorithm and a diagnosis index for the structural uplift of the tunnel is constructed on the basis of the aforementioned residuals, thereby achieving a diagnosis of the structural uplift of the water-rich shield tunnel. Finally, the proposed method is validated using a synthetic numerical simulation and field monitoring data from an actual tunnel project.