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Application of Machine Learning to Assist a Moisture Durability Tool

Author

Listed:
  • Mikael Salonvaara

    (Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA)

  • Andre Desjarlais

    (Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA)

  • Antonio J. Aldykiewicz

    (Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA)

  • Emishaw Iffa

    (Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA)

  • Philip Boudreaux

    (Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA)

  • Jin Dong

    (Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA)

  • Boming Liu

    (Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA)

  • Gina Accawi

    (Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA)

  • Diana Hun

    (Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA)

  • Eric Werling

    (Building Technologies Office, U.S. Department of Energy, Washington, DC 20585, USA)

  • Sven Mumme

    (Building Technologies Office, U.S. Department of Energy, Washington, DC 20585, USA)

Abstract

The design of moisture-durable building enclosures is complicated by the number of materials, exposure conditions, and performance requirements. Hygrothermal simulations are used to assess moisture durability, but these require in-depth knowledge to be properly implemented. Machine learning (ML) offers the opportunity to simplify the design process by eliminating the need to carry out hygrothermal simulations. ML was used to assess the moisture durability of a building enclosure design and simplify the design process. This work used ML to predict the mold index and maximum moisture content of layers in typical residential wall constructions. Results show that ML, within the constraints of the construction, including exposure conditions, does an excellent job in predicting performance compared to hygrothermal simulations with a coefficient of determination, R 2 , over 0.90. Furthermore, the results indicate that the material properties of the vapor barrier and continuous insulation layer are strongly correlated to performance.

Suggested Citation

  • Mikael Salonvaara & Andre Desjarlais & Antonio J. Aldykiewicz & Emishaw Iffa & Philip Boudreaux & Jin Dong & Boming Liu & Gina Accawi & Diana Hun & Eric Werling & Sven Mumme, 2023. "Application of Machine Learning to Assist a Moisture Durability Tool," Energies, MDPI, vol. 16(4), pages 1-20, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:2033-:d:1072962
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    References listed on IDEAS

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    1. Astrid Tijskens & Hans Janssen & Staf Roels, 2019. "Optimising Convolutional Neural Networks to Predict the Hygrothermal Performance of Building Components," Energies, MDPI, vol. 12(20), pages 1-18, October.
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