Resilience Assessment: A Performance‐Based Importance Measure

The resilience of a system can be considered as a function of its reliability and recoverability. Hence, for effective resilience management, the reliability and recoverability of all components which build up the system need to be identified. After that, their importance should be identified using an appropriate model for future resource allocation. The critical infrastructures are under dynamic stress due to operational conditions. Such stress can significantly affect the recoverability and reliability of a system‘s components, the system configuration, and consequently, the importance of components. Hence, their effect on the developed importance measure needs to be identified and then quantified appropriately. The dynamic operational condition can be modeled using the risk factors. However, in most of the available importance measures, the effect of risk factors has not been addressed properly. In this paper, a reliability importance measure has been used to determine the critical components considering the effect of risk factors. The application of the model has been shown through a case study

Quantify resilience enhancement of UTS through exploiting connect community and internet of everything emerging technologies

This work aims at investigating and quantifying the Urban Transport System (UTS) resilience enhancement enabled by the adoption of emerging technology such as Internet of Everything (IoE) and the new trend of the Connected Community (CC). A conceptual extension of Functional Resonance Analysis Method (FRAM) and its formalization have been proposed and used to model UTS complexity. The scope is to identify the system functions and their interdependencies with a particular focus on those that have a relation and impact on people and communities. Network analysis techniques have been applied to the FRAM model to identify and estimate the most critical community-related functions. The notion of Variability Rate (VR) has been defined as the amount of output variability generated by an upstream function that can be tolerated/absorbed by a downstream function, without significantly increasing of its subsequent output variability. A fuzzy based quantification of the VR on expert judgment has been developed when quantitative data are not available. Our approach has been applied to a critical scenario (water bomb/flash flooding) considering two cases: when UTS has CC and IoE implemented or not. The results show a remarkable VR enhancement if CC and IoE are deploye

Resilience of Hierarchical Critical Infrastructure Networks

Concern over the resilience of critical infrastructure networks has increased dramatically over the last decade due to a number of well documented failures and the significant disruption associated with these. This has led to a large body of research that has adopted graph-theoretic based analysis in order to try and improve our understanding of infrastructure network resilience. Many studies have asserted that infrastructure networks possess a scale-free topology which is robust to random failures but sensitive to targeted attacks at highly connected hubs. However, many studies have ignored that many networks in addition to their topological connectivity may be organised either logically or spatially in a hierarchical system which may significantly change their response to perturbations. In this paper we explore if hierarchical network models exhibit significantly different higher-order topological characteristics compared to other network structures and how this impacts on their resilience to a number of different failure types. This is achieved by investigating a suite of synthetic networks as well as a suite of ‘real world’ spatial infrastructure networks

Indicator-based method to evaluate community resilience

The capacity of a community to react and resist to an emergency is strictly related to the proper functioning of its own infrastructure systems. A better understanding of critical infrastructure architecture is necessary for defining measures to achieve a better resilience against threats (natural and human threats) in an integrated manner. For this purpose, indicators are perceived as important instruments to measure the resilience of infrastructure systems. Many research activities have been focusing on developing reliable indicators that could be applied at different scales, but research on resilience, which is a multidimensional and transformative concept, is still in the early stages of development. Developing a comprehensive, standardized set of resilience indicators is obviously difficult for such a dynamic, constantly re-shaping and context-dependent concept, Previous studies have highlighted the importance of conceptual frameworks to guide the selection of the indicators, so following the same trend this paper describes the procedure for selecting the proper indicators for community resilience within the PEOPLES framework (Cimellaro et. al 2009). PEOPLES is a holistic framework for defining and measuring disaster resilience of communities at various scales. It is divided into seven dimensions, and each dimension is further divided into several components. An integrated approach is presented that combines both quantitative and qualitative as well as outcome and process indicators, addressing a broad variety of issues such as the security, the geo-politics, the sociology, economy, etc. The methodology classifies the indicators’ location within the seven PEOPLES dimensions and provides a structure for creating a condensed list of indicators. Each indicator is linked to a measure allowing it to be quantified. The measures are expressed by serviceability functions rather than scalar values in order to allow a dynamic measurement of the indicators. Finally, the proposed indicators are weighted and then aggregated into a single serviceability function that describes the functionality of the community in time. The developed methodology has been tested on the critical infrastructures of San Francisco, USA, in order to assess their level of resiliency. Results of the case study show that the methodology introduced to compute the resiliency allows decision makers to derive key-indicators of community resilience that are applicable on a higher level of societal resilience, across different contexts and hazard types (attacks, accidents, etc.). The present work contributes to this growing area of research as it provides a universal tool to quantitatively assess the resilience of communities at multiple scales