Resilience Engineering for Urban Tunnels

Structural health monitoring (SHM) technologies are useful for rapid structural condition assessment, especially for hidden locations after major hazardous events. This chapter describes a strategy for enhancing structural resilience by integrating innovative SHM technologies focused on a few structural fuse members within resilient structures. The proposed framework would be helpful in implementing SHM technologies into civil infrastructures by quantifying and comparing the resilience metrics of different SHM options. The chapter presents preliminary research into integrating SHM technologies with resilient civil infrastructure design and establishing a resilience metric for rating candidate SHM technologies. An explicit SHM resilience metric is proposed, which could be used to quantify the benefits of installing replaceable fuse devices and associated monitoring technology. To address resilience issues, such as uninterrupted use even after strong earthquakes, self-centering behavior has been incorporated into self-centering structures through post-tensioning.

This chapter presents two examples of reliability analyses in mechanized tunneling. The first example shows the capabilities of numerical reliability analysis in a tunnel project’s design stage. The second example presents a hybrid surrogate modeling approach for real-time predictions of interval settlement fields. For real-time reliability analyses, fast results are required, which numerical efficient surrogate models achieve based on the finite element model and monitoring data. The chapter also presents approaches for reliability analyses in mechanized tunneling, which can be applied in tunnel projects’ design and construction phases. The numerical reliability analyses are based on a process-oriented finite element simulation model. The application simulation-and-monitoring-based assistant for real-time steering in mechanized tunneling is a helpful tool for decision making related to adjusting the support and grouting pressures during tunnel construction.

This chapter presents an integrated resiliency assessment model for tunnel deformational performance that fully uses field data from non-intrusive inspection and real-time monitoring systems. When a non-intrusive inspection that uses vehicle and real-time monitoring based on wireless sensing network (WSN) techniques is applied to real tunnels, the amount of measured data reaches the gigabyte level for one night’s inspection or monitoring. Based on these inspection and monitoring data, a field-data-based resiliency model is used to analyze the resiliency behavior of tunnel structural performance. The chapter also proposes a WSN for tunnel convergence of horizontal diameter and presents the associated sensor node, network, and transformation laws. It describes the framework for applying the inspection and monitoring data to the resilience analysis model and presents real application examples.

This chapter introduces a quantitative approach to evaluating failure consequences by estimating the reliability of systems with cascading failures. The survival signature is highly advantageous in traditional analysis techniques, as it can be calculated once, ahead of the analysis, and used to perform reliability analysis for several scenarios on the same network without reevaluating the network structure. The chapter also introduces a novel methodology to assess cascading failures between systems. This method provides a structured approach to a quantitative analysis of failure consequences, up to an economic loss for a rigorous risk analysis of complex interconnected systems and networks. The chapter overviews the survival signature and the Monte Carlo approximation of network reliability and deals with the probabilistic characteristics of failures with dependencies. The chapter also presents some numerical examples.

This chapter analyzes the European transportation network, focusing on socioeconomic conditions and interconnections among transportation systems. It aims to enhance the resilience of real-world traffic infrastructure, which is modeled as a macro network consisting of interdependent mesosystems such as railway or road systems. The traffic through the airport tunnel in Germany’s capital of Berlin is analyzed as a real case scenario of random impact at a neuralgic point. Neuralgic points such as important bridges or tunnels can be described at the nano level. To develop an adequate model of the macro network, heavy impacts that have already occurred in meso-level traffic systems are analyzed to assess their implications for the macro level. The chapter focuses on the capital’s passenger mobility, modeled by the meso-level rail and road systems.

This chapter adopts a network perspective, discussing the ever-growing need for fundamental interdisciplinary study of critical infrastructure networks, efficient methods for estimating their reliability, and cost-effective strategies for enhancing their resiliency. It highlights some of the main challenges involved, including cascading failures, feedback loops, and cross-sector interdependencies. The chapter also formulates a general network reliability problem, explaining why it is computationally very challenging, and briefly describes a recently introduced network reliability method. The chapter discusses extending Subset Simulation—an efficient classical reliability method for estimating small failure probabilities of structures—to networks. The key idea behind Subset Simulation is to decompose a very small failure probability into a product of larger conditional probabilities, each of which can be efficiently estimated by a Monte Carlo-like method.

The Decision Aids for Tunneling (DAT) is a computer-based tool that captures and processes uncertainty in geologic/geotechnical conditions and in the construction process to produce time-cost scattergrams and time-distance diagrams reflecting this uncertainty. These results can then be used, for instance, for risk analysis and management. In the context of the DAT and similar approaches, resilient design focuses on the construction process. More specifically, the construction process should be adaptable to minimize risks and/or satisfy particular boundary conditions. The chapter schematically shows a more encompassing approach that involves combining the DAT with a new optimization tool using simulated annealing. For instance, an alignment is selected based on transportation requirements, followed by the DAT time-cost determination. Varying the alignment while satisfying all boundary conditions allows an optimum to be obtained.

To create a clearly structured research agenda for developing a dynamic risk control approach, the workshop covers three major topics: smart sensing, robust design, and uncertainty modeling. This chapter comprises three general reports from each group prepared by the corresponding recorders. The three reports cover monitoring for resiliency of urban tunnels, robust design of tunnels, and modeling and management of uncertainties. Smart monitoring for tunnel resilience covers the following three important themes: monitoring as the data collection and analytic tool to characterize the tunnel’s structural health, tunnel or pipeline resilience as the definition or metric to quantify the tunnel’s ability to recover from hazardous events, and interaction between these two areas as the bridge to communicate between the monitoring and resilience groups. The chapter describes a series of components that are involved in the robust design.

This chapter presents the following research recommendations for urban underground engineering and lifeline projects: monitoring for urban tunnel resiliency, robust monitoring and maintenance for durability, and resilience engineering at system scale. Monitoring for urban tunnel resiliency aims to conduct fundamental and application-oriented research, allowing the risk associated with shield tunnels to be controlled and cost-effectiveness to be achieved during the risk management process. Robust monitoring and maintenance for durability aims to discover the accurate response of shield tunnels exposed to environmental uncertainties after maintenance. It aims to find a robust monitoring strategy that can detect all needed information with lowest cost and energy consumption. An international research consortium has been formed to drive developments in the resilience engineering at system scale project.