Reliability Approach in Economic Assessment of Adapting Infrastructure to Climate Change

Sooji Ha, Kyeongseok Kim, Kinam Kim, Hoyoung Jeong, Hyoungkwan Kim

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)

Abstract

Climate conditions have impacts on performance of infrastructures, and the impacts are expected to increase in the future due to climate change. Future climate conditions should be taken into account in establishing adaptation strategies for infrastructures. However, project-level performance analyses that incorporate climate-change scenarios have not been of major concern in economic assessment. Addressing this issue, this study presents a reliability approach to assess the economic cost and benefit of adapting infrastructure to climate change. Using climate-change scenarios, a deterioration modeling methodology is proposed to reflect the impact of climate conditions on the performance of infrastructure. The gamma process, which has been widely used for modeling monotonic deteriorations, was used within the methodology. Expected damage cost by climate-related risks was also estimated. Decision makers can apply the proposed methodology to the adaptation of various infrastructures under climate change.

Original languageEnglish
Article number04017022
JournalJournal of Management in Engineering
Volume33
Issue number5
DOIs
Publication statusPublished - 2017 Sept 1

Bibliographical note

Funding Information:
This work was supported by grants from the National Research Foundation of Korea (NRF), funded by the Ministry of Science, ICT and Future Planning (MSIP) of the Republic of Korea (No. 2011-0030040 and NRF-2014R1A2A1A11052499). The authors are grateful to Seonghui Mo of the Department of Statistics at Seoul National University for her academic discussions.

Publisher Copyright:
© 2017 American Society of Civil Engineers.

All Science Journal Classification (ASJC) codes

  • Industrial relations
  • Engineering(all)
  • Strategy and Management
  • Management Science and Operations Research

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