Verification of Non-Destructive Assessment of Moisture Content of Historical Brick Walls Using Random Forest Algorithm

Abstract

The paper presents the results of verification of the suitability of the random forest algorithm for the non-invasive assessment of excessively damp and salty historical brick walls. A new method of such quantitative assessment was developed and recently published by the author for the purpose of conducting research in buildings where destructive intervention is not possible due to conservation restrictions. However, before implementing the developed method into construction practice, it requires further validation. The conducted research showed that among all analyzed machine learning algorithms, the random forest algorithm is the most predisposed for the non-invasive evaluation of the Umc mass moisture content of brick walls. Data sets from archival research and experimental tests conducted in two historical buildings were used to verify the usefulness of this algorithm. This usefulness was confirmed by the obtained satisfactory values of the linear correlation coefficient R, which amounted to 0.801 for the first building and 0.803 for the second one. Moreover, it was also proved by the obtained low values of medians of the absolute errors |Δf| equal to 1.79% and 1.46%, and also by the not too high (for an in situ study) medians of the relative errors |RE| equal to 16.70% and 13.75%.