Application of Machine Learning to Assist a Moisture Durability Tool

Mikael Salonvaara (), Andre Desjarlais, Antonio J. Aldykiewicz, Emishaw Iffa, Philip Boudreaux, Jin Dong, Boming Liu, Gina Accawi, Diana Hun, Eric Werling and Sven Mumme
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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

Energies, 2023, vol. 16, issue 4, 1-20

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.

Keywords: building envelope; moisture; durability; design; machine learning; optimization; artificial intelligence (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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