Risk Identification and Assessment Methodology for Restoration Work Utilizing Unmanned Vehicles at Disaster Scenes
- Authors
- Kim, Byeol; Lee, Joosung; Ueda, Jun; Cho, Yong Kwon; Han, Changsoo; Ahn, Yonghan
- Issue Date
- Dec-2018
- Publisher
- American Society of Civil Engineers (ASCE)
- Citation
- Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019, pp 322 - 329
- Pages
- 8
- Indexed
- SCIE
SCOPUS
- Journal Title
- Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019
- Start Page
- 322
- End Page
- 329
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/4688
- DOI
- 10.1061/9780784482445.041
- ISSN
- 0000-0000
- Abstract
- Nuclear power plant accidents such as those at Fukushima and Chernobyl release a significant amount of hazardous radiation. It takes a long time to recover from such an accident and the difficulty of the task is very high due to the many risks. Restoration work using unmanned vehicles protects workers from the risk of secondary accidents such as radiation exposure and building collapses. Unmanned tasks in high-risk environments should be performed in accordance with carefully planned and optimized work scenarios that prioritize specific tasks. This study examined the non-radiological and radiological risks, accessibility, task difficulty, and efficiency that determine work priorities in such cases and developed a methodology to assess the risks associated with specific unmanned restoration tasks. The proposed risk assessment methodology is based on a risk matrix that can be used to derive work priorities for each zone at a post-disaster nuclear site. © 2019 American Society of Civil Engineers.
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