Machine learning models applied to moisture assessment in building materials

• Tipo: Artigo em Revista Científica Internacional
• Data: 2023

Autores

Dafico, LCM (Autor) Outra; Eva Barreira (Autor) FEUP; Almeida, RMSF (Autor) Outra; Vicente, R (Autor) Outra

Revista

Título: Construction and Building Materials

Vol. 405

ISSN: 0950-0618

Editora: Elsevier

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00Z-23E

DOI: 10.1016/j.conbuildmat.2023.133330

Abstract (EN): 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 17