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Cited by 678

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Measuring Improvements in the Disaster Resilience of Communities

Chang, Stephanie E., Shinozuka, Masanobu·2004·Earthquake Spectra

Abstract

This paper demonstrates the concept of disaster resilience through the development and application of quantitative measures. As the idea of building disaster‐resilient communities gains acceptance, new methods are needed that go beyond estimating monetary losses and that address the complex, multiple dimensions of resilience. These dimensions include technical, organizational, social, and economic facets. This paper first proposes resilience measures that relate expected losses in future disasters to a community's seismic performance objectives. It then demonstrates these measures in a case study of the Memphis, Tennessee, water delivery system. An existing earthquake loss estimation model provides a starting point for quantifying resilience. The analysis compares two seismic retrofit strategies and finds that only one improves community resilience over the status quo. However, it does not raise resilience to an adequate degree. The exercise demonstrates that the resilience framework can be valuable for guiding mitigation and preparedness efforts. However, to fully implement the concept, new research on resilience is needed that goes beyond loss estimation modeling.

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Metadata

Authors
Chang, S. E., & Shinozuka, M.
Venue
Earthquake Spectra
Publisher
Wiley
Published
2004-08
Volume
20
Issue
3
Pages
739-755
ISSN
  • 1944-8201 (ISSN-L)
  • 8755-2930
OpenAlex
W2161477551
Cited by
678
References
5
FWCI
14.49
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Measuring Improvements in the Disaster Resilience of Communities. https://app.cochat.ai/papers/10.1193/1.1775796
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Chang, S. E., & Shinozuka, M. (2004). Measuring Improvements in the Disaster Resilience of Communities. Earthquake Spectra. https://doi.org/10.1193/1.1775796

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.