A field case of operated shield tunnel disrupted by unexpected extreme surcharge in Shanghai is reported in this paper. The deformational responses of segmental lining, in terms of convergences, settlements and joint open width, are highlighted. The surcharge caused by unexpected dumped soils with a maximum height of 7 m is six times larger than the design value of surcharge. Three hundred and sixty segmental lining rings of the operated metro shield tunnel below this surcharge are severely affected. The measured horizontal convergence to outer-diameter ratio (ΔD/D_out) ranges from 16.6 to 35.7‰, which is far beyond the averaged value for normal condition, i.e. 5.1‰. The trend of tunnel deformation (e.g. horizontal convergence and settlement) varying with surcharge level is found to be significantly non-linear. Correspondingly, the longitudinal joints are also severely disrupted and have large open widths. The rubber packer for joint near-tunnel springline loses its function of waterproof due to the release of 6 mm pre-compressive deformation. The majority of steel bolts of joint near-tunnel crown have passed yield state, while some are fractured. However, the circumferential joints are found to be in a sound workability.

This paper presents a deep learning (DL)-based method for the instance segmentation of cracks from shield tunnel lining images using a mask region-based convolutional neural network (Mask R-CNN) incorporated with a morphological closing operation. The Mask R-CNN herein is divided into a backbone architecture, a region proposal network (RPN), and a head architecture for specification, and the implementation details are introduced. Compared with the current image processing methods, the proposed DL-based method efficiently detects cracks in an image while simultaneously generating a high-quality segmentation mask for each crack. A shield tunnel lining image dataset is established for crack instance segmentation task. The established dataset contains a total of 1171 labelled crack instances in 761 images. The morphological closing operation was incorporated into a Mask R-CNN to form an integrated model to connect disjoint cracks that belong to one crack. Image tests were carried out among four trained models to explore the effect of the morphological closing operation, network depth, and feature pyramid network on crack segmentation performance, and a relative optimal model is found. The relative optimal model achieves a balanced accuracy of 81.94%, a F₁ score of 68.68%, and an intersection over union (IoU) of 52.72% with respect to 76 test images.