e-Infrastructure and e-Services for Developing Countries. 8th International Conference, AFRICOMM 2016, Ouagadougou, Burkina Faso, December 6-7, 2016, Proceedings

Research Article

Classification of Water Pipeline Failure Consequence Index in High-Risk Zones: A Study of South African Dolomitic Land

  • @INPROCEEDINGS{10.1007/978-3-319-66742-3_15,
        author={Achieng Ogutu and Okuthe Kogeda and Manoj Lall},
        title={Classification of Water Pipeline Failure Consequence Index in High-Risk Zones: A Study of South African Dolomitic Land},
        proceedings={e-Infrastructure and e-Services for Developing Countries. 8th International Conference, AFRICOMM 2016, Ouagadougou, Burkina Faso, December 6-7, 2016, Proceedings},
        proceedings_a={AFRICOMM},
        year={2017},
        month={10},
        keywords={Pipeline failure Failure impacts Consequence index Dolomitic land Predictive modeling High-risk zones Water leakage},
        doi={10.1007/978-3-319-66742-3_15}
    }
    
  • Achieng Ogutu
    Okuthe Kogeda
    Manoj Lall
    Year: 2017
    Classification of Water Pipeline Failure Consequence Index in High-Risk Zones: A Study of South African Dolomitic Land
    AFRICOMM
    Springer
    DOI: 10.1007/978-3-319-66742-3_15
Achieng Ogutu1,*, Okuthe Kogeda1,*, Manoj Lall1,*
  • 1: Tshwane University of Technology
*Contact email: achienggrc8@gmail.com, kogedaPO@tut.ac.za, lallM@tut.ac.za

Abstract

Increasing numbers of pipeline breakdown experienced by utilities undoubtedly raise alarms concerning the anticipated failure consequences. Seemingly mild, these consequences can however, fluctuate to severe or fatal, especially in high risk locations. Utility personnel are therefore pressured to employ up-to-par operational policies in attempt to minimize possible fatalities. This however, may be overwhelming considering inherent uncertainties that make it difficult to understand and adapt these consequences into utilities’ risk management structure. One way of handling such uncertainties is through the use of Bayesian Networks (BNs), which can comfortably combine supplementary information and knowledge. In this paper therefore, we present an overview of the causes and impacts of pipeline failure. We aggregate and classify failure consequences in a select high risk zone into four indexes; and finally, we outline how BNs can accommodate these indexes for pipeline failure prediction modeling. These indexes function as effective surrogate inputs where data is unavailable.