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This paper presents a general framework to assess the resilience of large and complex metro networks by quantitatively analyzing its vulnerability and recovery rapidity within unifying metrics and models. The connectivity performance of network is indicated by the network efficiency. The resilience of a metro network can be associated to the network performance loss triangle over the relevant timeline from the occurrence of a random or intentional disruption to full recovery. The proposed resilience model is applied to the Shanghai metro network with its 303 stations and 350 links as an example. The quantitative vulnerability analysis shows that the Shanghai metro with its L-space type of topology has a strong robustness regarding connectivity under random disruption but severe vulnerability under intentional disruption. This result is typical for small-world and scale-free networks such as the Shanghai metro system, as can be shown by a basic topological analysis. Considering the case of one disrupted metro station, both the vulnerability and resilience of the network depend not only on the node degree of the disrupted station but also on its contribution to connectivity of the whole network. Analyzing the performance loss triangle and the associated cost from loss of operational income and repair measures, an appropriate recovery strategy in terms of the optimum recovery sequence of stations and the optimum duration can be identified in a structured manner, which is informative and helpful to decision makers. [ABSTRACT FROM AUTHOR]

• 1.A well-documented settlement recovery case of shield tunnel is introduced. • 2.Grouting effect on deformation in spatial and temporal dimensions is revealed. • 3.Volume-based grouting efficiency is used to study the deformation characteristics. • 4.Grouting efficiency is low, horizontal convergence increases during the grouting. This paper presents a case study introducing the effect of soil grouting conducted to repair the longitudinal performance of shield tunnel in Shanghai. The maximum of cumulative settlement of the shield tunnel was approximately 15 mm with an averaged increasing rate as high as 0.50 mm/d caused by a new tunneling project underneath. The grouting reinforcement for soils under the invert of tunnel was thus carried out in the distorted section. A full process of deformational responses in terms of settlement and horizontal convergence to the grouting was measured to analyze the influence of grouting on performance recovery in the longitudinal direction. Volume-based grouting efficiency was defined for evaluating the contribution of grouting to structural deformation recovery. The results showed that the settlement is reduced linearly with the increasing of grouting volume. However, horizontal convergence increased during the grouting process and the maximum convergence increment was about 63 % of the magnitude of settlement reduction. Meanwhile, grouting efficiency was found to be relatively low. An average of 9.03 % of the grout volume is contributed to the recovery of settlement trough, while 2.75 % acted on the increase of convergence. Besides, continuous grouting can effectively recover tunnel settlement and remain a safe state. [ABSTRACT FROM AUTHOR]

The inherent spatial variability of soil properties is the main sauces of uncertainties in the site investigation, and it is commonly characterized using random field theory. In the context of random fields, the scale of fluctuation (SOF) is a significant parameter to reflect the spatial correlation between the two points of soil properties. However, it is challenging to estimate the SOF value accurately, especially when there are only limited project-specific test results, such as cone penetration test (CPT) data. This study aims to develop a convolutional neural network (CNN) approach to estimate the vertical SOF based on limited CPT data. The CNN model was constructed and trained by the simulated 15,000 CPT samples using random fields. The results show that the CNN model has excellent performance for estimating vertical SOF. The approach is validated and illustrated through newly simulated CPT data, eight real CPT data obtained from the literature, and three CPT data collected from the Shanghai site. The proposed scale factor method can solve the mismatch between the actual CPT depth and the required depth for input data of the CNN model, making the CNN model more widely applicable. • A CNN approach is developed to estimate vertical SOF for soil properties. • The approach is validated and illustrated using both real and simulated data. • The proposed scale factor method can make CNN model more widely applicable. • Promising estimation performance is achieved. [ABSTRACT FROM AUTHOR]