Optimized neural network based carbonation prediction model

Woubishet Z. Taffese, Fahim Al-Neshawy, Esko Sistonen, Miguel Ferreira

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    Abstract

    Concrete carbonation is one of the major causes of steel corrosion in reinforced concrete structure that can lead to shortened service life. Several carbonation prediction models including mathematical and neural network are available. The mathematical models are simplified and do not take all influential parameters of concrete materials into consideration. Most of the existing neural network based carbonation prediction models do not employ all parameters that influence the microstructural properties of the concrete. They also failed to perform certain essential steps during the model development, which in turn degrade their performance. In this work, novel neural network based carbonation prediction model is proposed. The model selects most relevant parameters, and removes irrelevant and/or redundant features from the original input data to build robust learning models. The performance evaluation of the model shows that the proposed carbonation prediction model predicts reasonably well with increased generalization ability.
    Original languageEnglish
    Title of host publicationInternational Symposium Non-Destructive Testing in Civil Engineering (NDT-CE) September 15 - 17, 2015, Berlin, Germany
    Pages1074-1083
    Number of pages10
    Publication statusPublished - 2015
    MoE publication typeA4 Article in a conference publication
    EventInternational Symposium Non-Destructive Testing in Civil Engineering, NDTCE 2015 - Berlin, Germany
    Duration: 15 Sep 201517 Sep 2015
    https://www.ndt.net/search/docs.php3?MainSource=178&sessionID=1200 (International Symposium Non-Destructive Testing in Civil Engineering (NDTCE 2015), 15-17 Sep 2015, Berlin, Germany)

    Publication series

    SeriesNDT.net

    Conference

    ConferenceInternational Symposium Non-Destructive Testing in Civil Engineering, NDTCE 2015
    Abbreviated titleNDTCE 2015
    CountryGermany
    CityBerlin
    Period15/09/1517/09/15
    Internet address

    Keywords

    • concrete carbonation
    • carbonation prediction
    • modelling
    • neural network

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  • Cite this

    Taffese, W. Z., Al-Neshawy, F., Sistonen, E., & Ferreira, M. (2015). Optimized neural network based carbonation prediction model. In International Symposium Non-Destructive Testing in Civil Engineering (NDT-CE) September 15 - 17, 2015, Berlin, Germany (pp. 1074-1083). NDT.net http://www.ndt.net/article/ndtce2015/papers/186_taffese_woubishet.pdf