Proposal for Prediction Method of Maximum Damage of Major Accidents by Using Risk Curve
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- FUKUDA Takabumi
- National University,Faculity of Engineering
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- OFUCHI Taichi
- National University,Faculity of Engineering
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- KASAI Naoya
- National University,Faculity of Engineering
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- SEKINE Kazuyoshi
- National University,Faculity of Engineering
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- HANAYASU Shigeo
- National Institute of Industrial Safety
Bibliographic Information
- Other Title
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- リスク曲線を用いた重大災害の発生規模予測法の提案
- リスク キョクセン オ モチイタ ジュウダイ サイガイ ノ ハッセイ キボ ヨソクホウ ノ テイアン
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Description
This paper concerns the statistical analysis of industrial accidents in order to assess the safety management system by highlighting the extreme events in large-scale accidents. The authors had proposed the use of risk curve to assess the performance of safety management systems and effects of safety measures for industries. Based on our previous studies, the authors investigate the application of the Frechet plot in order to predict the maximum magnitude in a certain period on condition that the safety management level is the same. Through the analysis with actual accidents data and series of percolate simulations, possibility to predict the expected maximum magnitude of accidents by applying the extreme value theory is shown. For the practical point of view, to evaluate the predictability of the expected damage loss with single risk curve is important. Therefore, distributions of the predicted maximum damages are observed. As the result, prediction for long term, such as 10 years, is impossible but this method might be applicable to the rough estimation for short term, such as 5 years. Through these considerations, it is demonstrated that the proposed methodology is applicable to assess the safety management level.
Journal
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- Journal of High Pressure Institute of Japan
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Journal of High Pressure Institute of Japan 43 (2), 75-84, 2005
HIGH PRESSURE INSTITUTE OF JAPN(HPI)
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Keywords
Details 詳細情報について
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- CRID
- 1390001204617260800
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- NII Article ID
- 10015498044
- 130004446745
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- NII Book ID
- AN00011762
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- ISSN
- 13479598
- 03870154
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- NDL BIB ID
- 7313378
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- Text Lang
- ja
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- Data Source
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- JaLC
- NDL Search
- CiNii Articles
- KAKEN
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- Abstract License Flag
- Disallowed