Society For Risk Analysis Annual Meeting 2017

Session Schedule & Abstracts


* Disclaimer: All presentations represent the views of the authors, and not the organizations that support their research. Please apply the standard disclaimer that any opinions, findings, and conclusions or recommendations in abstracts, posters, and presentations at the meeting are those of the authors and do not necessarily reflect the views of any other organization or agency. Meeting attendees and authors should be aware that this disclaimer is intended to apply to all abstracts contained in this document. Authors who wish to emphasize this disclaimer should do so in their presentation or poster. In an effort to make the abstracts as concise as possible and easy for meeting participants to read, the abstracts have been formatted such that they exclude references to papers, affiliations, and/or funding sources. Authors who wish to provide attendees with this information should do so in their presentation or poster.

Common abbreviations

T2-F
Symposium: Engineering and Modeling of Resilience

Room: Salon FG   10:30 am–12:00 pm

Chair(s): Hiba Baroud   hiba.baroud@vanderbilt.edu

Sponsored by Engineering and Infrastructure Specialty Group

Modeling and analysis of the resilience of systems garnered an increased interest among practitioners and researchers in recent years. These systems include infrastructure networks, communities, and processes, among others. As resilience definitions and metrics are developing and becoming more extensive, there is a need to develop frameworks and tools that provide the ability to quantify, measure, and analyze resilience as a function of deterioration and uncertainty due to natural and man-made hazards, as well as constraints such as the availability of resources. In addition, modeling the resilience of a particular system is generally focused on the behavior of the system during the response and recovery phase which has a stochastic nature. As such, accounting for the uncertainty is a significant component of studying systems resilience. This session is focused on advances in engineering and modeling of resilience, more specifically mathematical modeling, data analytics, and optimization approaches that are applied to model the resilience of systems. Methodological topics include but are not limited to risk analysis, reliability, graph theory, uncertainty quantification, stochastic optimization, network optimization, statistical learning, interdependency modeling, and decision analysis. Application areas include critical infrastructure systems such as energy, water, transportation, communications, and manufacturing, among others, as well as community resilience and social vulnerability.



T2-F.1  10:30 am  Emergence of Antifragility by Optimum Postdisruption Restoration Planning of Infrastructure Networks. Fang Y, Sansavini G*; ETH Zurich   sansavig@ethz.ch

Abstract: A system is antifragile if its performance improves as the result of exposure to stressors, shocks or disruptions. This behavior is typical of complex systems and it is not usually exhibited by engineered technical systems. In fact, technical systems can display anti-fragility when new investments are allocated, e.g. after disasters. This study proposes an optimization model for the post-disaster restoration planning of infrastructure networks, taking into account the possibility of combining the construction of new components and the repair of failed ones. The strategic goal is to determine the optimal target system structure so that the performance of the target system is maximized under the constraints of investment cost and network connectivity. The problem is formulated as a mixed-integer binary linear programming (MILP) and an efficient Benders decomposition algorithm is devised to cope with computational complexity of its solution. The proposed approach is tested on a realistic infrastructure network: the 380kV power transmission grid of Northern Italy. The results show that the restored network can achieve an improved functionality as compared to the original network if new components are constructed and some failed components are not repaired, even when the former is much more expensive than the latter. Therefore, antifragility provides an opportunity for the system to meet future service demand increases, and a perspective under which disruptions can be seen as chances for system performance improvements.

T2-F.2  10:50 am  Metrics for Resilience: What Are We Really Measuring? MacKenzie CA*; Iowa State University   camacken@iastate.edu

Abstract: Resilience has become quite the buzzword in the risk analysis and disaster management communities. Consequently, many different metrics and ways to measure resilience has been proposed in a wide variety of fields. This talk will focus on some of the more popular metrics for resilience and explore what these metrics mean for decision making. Ultimately, we want to ensure that metrics for resilience align with a decision maker's fundamental objectives and do not reflect means objectives or even inputs.

T2-F.3  11:10 am  Measuring community recovery rate with sparse data: a comparison of multiple approaches. Yu J*, Baroud H; Vanderbilt University   jinzhu.yu@vanderbilt.edu

Abstract: Resilience is often referred to as the ability to recover from the occurrence of a disruptive event. Various metrics have been developed to measure resilience for different types of hazards, but a common issue in quantifying those metrics and modeling them using data-driven methods is the lack of recovery data from historical events. Sparse data result in challenges in measuring and predicting resilience metrics within an acceptable level of accuracy. In this paper, the rate of recovery to the level prior to the occurrence of a disruptive event is used as the metric for resilience. Three statistical models are considered, hierarchical Bayesian model, Bayesian kernel model, and multivariate Poisson generalized linear model, to model the recovery rate with sparse data. The three methods are compared in terms of goodness of fit and prediction accuracy. Deviance and log-likelihood are used as the metrics for the goodness of fit, and root mean square error and normalized root mean square error are used as the metrics for prediction accuracy. The accurate estimation of recovery parameters from disasters improves recovery management, such as the optimization of resource allocation. We illustrate this work using a case study of Shelby County in Tennessee.

T2-F.4  11:30 am  An Indicator-Based Assessment of Community Resilience to Failure of Flood Protection Infrastructure. Gillespie-Marthaler L*, Camp J, Baroud H, Abkowitz M; Vanderbilt University   leslie.gillespie-marthaler@vanderbilt.ed

Abstract: Community resilience is defined herein as the ability to resist systemic disruption, recover, adapt, and/or transform given adverse situations in order to maintain desired performance while simultaneously considering and the availability of sustainability capital. Flood, in its many variations, is a significant hazard for communities worldwide. This presentation examines flood hazard associated with failure of flood protection infrastructure within the U.S. through a case study approach. Criteria are developed for risk characterization, and an initial assessment is conducted using multiple methods to identify study locations considered to be at high risk. Hazard scenarios and expected consequences are evaluated for these locations, from which community resilience is determined using a foundational set of indicators and associated quantitative and qualitative measures. The efficacy of using these indicators and associated measures is considered based on their sufficiency and appropriateness with regard to: 1) association with various community sub-systems; 2) alignment with attributes of complex system resilience, and 3) resilience priority. Results from this work are used to develop community resilience strategies and to assist in operationalizing assessment processes for community resilience.



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