The present paper discusses the application principles of value of information theory in adaptive metamodeling for reliability analysis. Metamodeling for reliability purposes has become particularly relevant in recent years. The usage of metamodels allows surrogating the, costly to evaluate, performance functions of engineering structures. Adaptive Kriging procedures are examples of the successful application of metamodel- ing in reliability analysis. Efficient adaptive Kriging involves the usage of some notion of improvement in what ultimately is an unsupervised decision making scheme that selects points to enrich the model. Therefore, the decision to select a point to enrich the experimental design should consider the utility of each candidate in the expectation of improvement of the metamodeling accuracy. Within this context, a comprehensive discussion on the application of value of information for reliability metamodeling is presented. Since the candidate points and surrogate are jointly built in a virtually costless model, it is possible to know the virtual outcome of the enrich- ment decisions. In many circumstances, points in the experimental design may provide redundant information. Furthermore, a priori knowledge on the performance function may be applied to weight the expected outcome of exploration and exploitation. Value of information considerations adds value to reliability metamodeling that uses adaptive methods, and is of interest for efficient design and optimization of complex structures, such as bridge structures. @en

This paper aims to move forward in the understanding of resilience by improving the quality of available information during the decision-making process. A dynamic methodology together with a bounded travel time cost function is used to quantify the transport resilience, and an analysis of the main parameters is performed by a mapping of possible scenarios and a sensitivity analysis. Due to the complexity of the model, a recent methodology for the sensitivity analysis is presented. This approach is a bi-phase sensitivity analysis with a combination of a local and a global method. This allows an early detection of the parameters that will have a larger impact on the network performance when the hazard occurs, and together with the mapping strategy, make possible to select the best ways to increase the resilience of the transport network at any stage of the process, i.e., before during and after the damaging event.@en

The concept of intrinsic vulnerability of a traffic network is defined for the first time in this paper. Intrinsic vulnerability is the susceptibility to incidents characterised by a probability of occurrence in space and time of difficult estimation, which can result in considerable reduction or loss of the system functionality. Given the nature of this type of vulnerability, its assessment might arise as a major problem. Therefore, this paper investigates the assessment of the intrinsic vulnerability of a traffic network through a set of quantifiable indicators, i.e., accessibility and reliability. Moreover, it is of interest to determine whether the selected indicators are sufficient to assess the intrinsic vulnerability or if there is any significant missing aspect to be considered. A new methodology based on structured elicitation of multivariate uncertainty from experts is presented to address these issues, allowing the estimation of the intrinsic vulnerability and its probabilistic relationship with the indicators accessibility and reliability. Although applied to the case of the metric intrinsic vulnerability, the proposed methodology emerges as an effective tool to understand other traffic descriptors of difficult evaluation such as resilience.@en