Performance comparison for pipe failure prediction using artificial neural networks

S. Kerwin, Borja Garcia de Soto, B. T. Adey

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Infrastructure managers must decide on the replacement timing of buried pipes in water distribution networks. These assets deteriorate resulting in failures and their associated consequences. In recent years, studies have investigated failure prediction models for pipes based on artificial neural networks (ANNs). These models are either generalized (i.e. trained with all pipe failures) or specialized (i.e. trained with certain pipe failures based on pipe material or failure history). It is currently unclear whether prediction accuracy is improved by developing several specialized ANNs compared to a single generalized one. To answer this question, four modelswere developed: a generalized model for cast iron (CI) and ductile iron (DI) pipes; a specialized model for CI pipes, a specialized model for CI pipes with no previous failures and a specialized model for CI pipes, which had previously failed. Overall, the study found minimal difference in performance between the generalized and specialized models.

    Original languageEnglish (US)
    Title of host publicationLife-Cycle Analysis and Assessment in Civil Engineering
    Subtitle of host publicationTowards an Integrated Vision - Proceedings of the 6th International Symposium on Life-Cycle Civil Engineering, IALCCE 2018
    EditorsDan M. Frangopol, Robby Caspeele, Luc Taerwe
    PublisherCRC Press/Balkema
    Pages1337-1342
    Number of pages6
    ISBN (Print)9781138626331
    StatePublished - Jan 1 2019
    Event6th International Symposium on Life-Cycle Civil Engineering, IALCCE 2018 - Ghent, Belgium
    Duration: Oct 28 2018Oct 31 2018

    Publication series

    NameLife-Cycle Analysis and Assessment in Civil Engineering: Towards an Integrated Vision - Proceedings of the 6th International Symposium on Life-Cycle Civil Engineering, IALCCE 2018

    Conference

    Conference6th International Symposium on Life-Cycle Civil Engineering, IALCCE 2018
    CountryBelgium
    CityGhent
    Period10/28/1810/31/18

    Fingerprint

    Pipe
    Neural networks
    Cast iron pipe
    Nodular iron
    Cast iron
    Electric power distribution
    Managers
    Water

    ASJC Scopus subject areas

    • Civil and Structural Engineering
    • Safety, Risk, Reliability and Quality

    Cite this

    Kerwin, S., Garcia de Soto, B., & Adey, B. T. (2019). Performance comparison for pipe failure prediction using artificial neural networks. In D. M. Frangopol, R. Caspeele, & L. Taerwe (Eds.), Life-Cycle Analysis and Assessment in Civil Engineering: Towards an Integrated Vision - Proceedings of the 6th International Symposium on Life-Cycle Civil Engineering, IALCCE 2018 (pp. 1337-1342). (Life-Cycle Analysis and Assessment in Civil Engineering: Towards an Integrated Vision - Proceedings of the 6th International Symposium on Life-Cycle Civil Engineering, IALCCE 2018). CRC Press/Balkema.

    Performance comparison for pipe failure prediction using artificial neural networks. / Kerwin, S.; Garcia de Soto, Borja; Adey, B. T.

    Life-Cycle Analysis and Assessment in Civil Engineering: Towards an Integrated Vision - Proceedings of the 6th International Symposium on Life-Cycle Civil Engineering, IALCCE 2018. ed. / Dan M. Frangopol; Robby Caspeele; Luc Taerwe. CRC Press/Balkema, 2019. p. 1337-1342 (Life-Cycle Analysis and Assessment in Civil Engineering: Towards an Integrated Vision - Proceedings of the 6th International Symposium on Life-Cycle Civil Engineering, IALCCE 2018).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Kerwin, S, Garcia de Soto, B & Adey, BT 2019, Performance comparison for pipe failure prediction using artificial neural networks. in DM Frangopol, R Caspeele & L Taerwe (eds), Life-Cycle Analysis and Assessment in Civil Engineering: Towards an Integrated Vision - Proceedings of the 6th International Symposium on Life-Cycle Civil Engineering, IALCCE 2018. Life-Cycle Analysis and Assessment in Civil Engineering: Towards an Integrated Vision - Proceedings of the 6th International Symposium on Life-Cycle Civil Engineering, IALCCE 2018, CRC Press/Balkema, pp. 1337-1342, 6th International Symposium on Life-Cycle Civil Engineering, IALCCE 2018, Ghent, Belgium, 10/28/18.
    Kerwin S, Garcia de Soto B, Adey BT. Performance comparison for pipe failure prediction using artificial neural networks. In Frangopol DM, Caspeele R, Taerwe L, editors, Life-Cycle Analysis and Assessment in Civil Engineering: Towards an Integrated Vision - Proceedings of the 6th International Symposium on Life-Cycle Civil Engineering, IALCCE 2018. CRC Press/Balkema. 2019. p. 1337-1342. (Life-Cycle Analysis and Assessment in Civil Engineering: Towards an Integrated Vision - Proceedings of the 6th International Symposium on Life-Cycle Civil Engineering, IALCCE 2018).
    Kerwin, S. ; Garcia de Soto, Borja ; Adey, B. T. / Performance comparison for pipe failure prediction using artificial neural networks. Life-Cycle Analysis and Assessment in Civil Engineering: Towards an Integrated Vision - Proceedings of the 6th International Symposium on Life-Cycle Civil Engineering, IALCCE 2018. editor / Dan M. Frangopol ; Robby Caspeele ; Luc Taerwe. CRC Press/Balkema, 2019. pp. 1337-1342 (Life-Cycle Analysis and Assessment in Civil Engineering: Towards an Integrated Vision - Proceedings of the 6th International Symposium on Life-Cycle Civil Engineering, IALCCE 2018).
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