Framework for analytical quantification of disaster resilience

By Gian Paolo Cimellaro, Andrei M. Reinhorn, M. Bruneau. 2010

1,503 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Emergency Medical Services, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/94afdd8880deff08893c467f15ab5b6479095261

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. Nikos D. Hatziargyriou
2. Dimitris N. Trakas
3. Mathaios Panteli
4. Pierluigi Mancarella
5. Zhenghua Wang
6. Enrico Zio
7. Ouyang Min
8. Michael Havbro Faber

In Emergency Medical Services

1. J. Stone
2. Enrico Zio
3. Shahin Rahimifard
4. Ouyang Min

In Sociology and Political Science

1. Zhenghua Wang
2. Enrico Zio
3. J. Stone
4. Shahin Rahimifard
5. Ouyang Min

Citations in the corpus

• Resilience assessment of interdependent infrastructure systems: With a focus on joint restoration modeling and analysis (2015, background): “area underneath the performance curve of systems in a disruptive event [16,17], and some others [15,18–21], is that the proposed metric is defined during a given period while others are defined for specific events during a specific restoration period without incorporating the event frequency.”
• Seismic resilience of concrete structures under corrosion (2015, neutral): “For the purpose of the discussion presented in this paper, resilience can be regarded as the capability of structures, infrastructure systems, and entire communities, to withstand the effects of extreme events and to recover efficiently the original performance and functionality [10, 14, 16, 17].”
• Power Systems Resilience Assessment: Hardening and Smart Operational Enhancement Strategies (2017, background): “2) traditionally used and modelled in past resilience studies [13-15].”
• The future of risk assessment (2018, methodology): “The outcomes of the 2005 World Conference on Disaster Reduction (WCDR) confirmed the significance of the entrance of the term resilience into the disaster discourse and gave birth to a new culture of disaster response [48].”
• On the Probabilistic Characterization of Robustness and Resilience (2017, background): “[5]- [14] Most often the suggested models are directed on the short term representation of the ability of the system to sustain and recover from disturbances, fast, without substantial loss of functionality and without the support from the outside.”
• Resilience in agri-food supply chains: a critical analysis of the literature and synthesis of a novel framework (2018, background): “Equally important is “Co-Learning” (IS19) which involves the procedures in place to aid system wide joint learning from both near misses and actual disruptions (King, 2008; Pettit et al., 2010).”