Abstract
Moisture-related defects hinder long-term building durability and must be prevented. Non-destructive techniques that measure the surface temperature of building materials have good potential for moisture analysis. This article presents machine learning models to predict the moisture content of materials according to their surface temperature using as input the material and the environmental conditions. Results showed that neural network models present a coefficient of determination higher than 0.96 and an error of less than 5%. The application of the models demonstrated that in brick and granite the model presented excellent results, but in limestone and concrete it enabled the identification of moisture only in the near-surface zone.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
