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Application of Machine Learning to Assist a Moisture Durability Tool

Journal Article · · Energies
DOI:https://doi.org/10.3390/en16042033· OSTI ID:1968687
 [1];  [1];  [1];  [1];  [1];  [1];  [1];  [1];  [1];  [2];  [3]
  1. Oak Ridge National Laboratory, Oak Ridge, TN (United States)
  2. US Department of Energy (USDOE), Washington DC (United States)
  3. Building Technologies Office, U.S. Department of Energy, Washington, DC 20585, USA
+ Show Author Affiliations
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, R2, over 0.90. Furthermore, the results indicate that the material properties of the vapor barrier and continuous insulation layer are strongly correlated to performance.
Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
Grant/Contract Number:
AC05-00OR22725
OSTI ID:
1968687
Journal Information:
Energies, Journal Name: Energies Journal Issue: 4 Vol. 16; ISSN 1996-1073
Publisher:
MDPICopyright Statement
Country of Publication:
United States
Language:
English

References (8)

Neural networks for metamodelling the hygrothermal behaviour of building components journal September 2019
A machine learning and deep learning based approach to predict the thermal performance of phase change material integrated building envelope journal July 2021
State-of-the-art on research and applications of machine learning in the building life cycle journal April 2020
Modeling of hygrothermal behavior for green facade's concrete wall exposed to nordic climate using artificial intelligence and global sensitivity analysis journal January 2021
Selecting durable building envelope systems with machine learning assisted hygrothermal simulations database journal November 2021
A rule-based expert system applied to moisture durability of building envelopes journal January 2018
Application of Support Vector Regression to the Prediction of the Long-Term Impacts of Climate Change on the Moisture Performance of Wood Frame and Massive Timber Walls journal April 2021
Optimising Convolutional Neural Networks to Predict the Hygrothermal Performance of Building Components journal October 2019

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Related Subjects

36 MATERIALS SCIENCE
artificial intelligence
building envelope
design
durability
machine learning
moisture
optimization