Application of Machine Learning to Assist a Moisture Durability Tool

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.

Automated summary

The design of moisture-durable building enclosures is complex, and hygrothermal simulations require in-depth knowledge. Machine learning was used to simplify the design process and predict the moisture durability of a building enclosure. The results show that machine learning accurately predicts performance compared to hygrothermal simulations.