Comparative analysis of strengths and limitations of infrastructure resilience measurement methods

Document Type

Conference Proceeding

Publication Title

Proceedings, Annual Conference - Canadian Society for Civil Engineering

Publication Date

1-1-2019

Abstract

Since the beginning of the 21st century, experts have increasingly used resilience analysis to assess the damages and performance of infrastructures suffering from disturbing events like natural and/or man-made hazards. The resilience of infrastructures is almost always affected by severe calamitous events, even though the damages are not always visible. Researchers have developed several methods, which have been recently adopted by the transportation sector, to define the physical condition and/or performance deviations of affected infrastructures by measuring their resilience. Throughout literature, few studies focus on comparative analysis based on the advantages and disadvantages of these models; therefore, the goal of this paper is to identify and analyze frameworks, based on their applicability and dimensions. To achieve this goal, a thorough review of literature was conducted to define the resilience concept and identify the frameworks. Several of the most recent resilience-measuring models and methods that relate to the technical aspects of the resilience of transportation infrastructures, including the Critical Infrastructure Resilience Decision Support System (CIR-DSS), and Cox�s proportional hazards regression model and resilience optimization model, were identified to conduct a comparative analysis. Findings of this study will help researchers explore current gaps in research on resilience of the transportation infrastructure sector, and will guide researchers in developing a resilience measurement model that incorporates all dimensions. This study will also be of great help to researchers and practitioners, as they adopt appropriate methods to measure the severity of damages and identify proactive strategies to reduce unintended consequences of disruptive events.

Volume

2019-June

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