Environmental Engineering Research

  • 제24권1호
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  • Pages.63-73
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  • 2019
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  • 1226-1025(pISSN)
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  • 2005-968X(eISSN)
대한환경공학회 (Korean Society of Environmental Engineers)
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Economic-based approach for predicting optimal water pipe renewal period based on risk and failure rate

  • Kim, Kibum (Department of Environmental Engineering, University of Seoul) ;
  • Seo, Jeewon (Department of Environmental Engineering, University of Seoul) ;
  • Hyung, Jinseok (Department of Environmental Engineering, University of Seoul) ;
  • Kim, Taehyeon (Department of Environmental Engineering, University of Seoul) ;
  • Kim, Jaehag (Metropolitan Water Supply Construction Office) ;
  • Koo, Jayong (Department of Environmental Engineering, University of Seoul)
  • 투고 : 2017.11.21
  • 심사 : 2018.05.30
  • 발행 : 2019.03.31
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초록

This study suggests a method for calculating the benefits of water pipe renewal based on an estimate of the water supply suspension risk. The proposed method based on five benefit items is more direct and specific than other benefit estimation methods. In addition, a methodology evaluating the economics of pipe renewal based on pipe failure rate is proposed for estimating the optimal renewal point from an economic perspective. By estimating the optimal renewal period based on a yearly benefit cost ratio per pipe in a case study area, it was possible to draft an optimal renewal plan for the subject region from an economic perspective. Compared with other methodologies, a reasonable optimal renewal period was derived from an economic point of view. The result of this study may be used to develop future water pipe renewal plans. Moreover, the proposed methodologies and results derived from this study can be applied to asset management plans.

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참고문헌

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피인용 문헌

  1. Cost-Benefit Prediction of Asset Management Actions on Water Distribution Networks vol.11, pp.8, 2019, https://doi.org/10.3390/w11081542
  2. Mechanical-Based Approach for Operational Risk Evaluation of Water Mains in Shanghai vol.11, pp.1, 2020, https://doi.org/10.1061/(asce)ps.1949-1204.0000429
  3. Teaching-Learning-Based Optimization of Neural Networks for Water Supply Pipe Condition Prediction vol.13, pp.24, 2019, https://doi.org/10.3390/w13243546