###### 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 \(U_{mc}\) 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 \(\mid\)\(\mid\)\(\mid\) equal to 1.79% and 1.46%, and also by the not too high (for an in situ study) medians of the relative errors \(\mid\)\(\mid\)\(\mid\) equal to 16.70% and 13.75%. historical brick walls; mass moisture; non-destructive testing; random forest algorithm +
Footnote †: journal: Applied Sciences  
The author's previous experience clearly shows that conducting moisture tests of brick walls in buildings under conservation protection is not an easy task, because interference in the structure of their historical tissue is possible to a very limited extent. This means that the free use of the destructive gravimetric method, which is currently considered to be the most reliable method of assessing the mass moisture content, is not possible [8; 9]. At the same time, such a situation forces the use of non-destructive methods, such as the dielectric, microwave [10] or thermal imaging methods [11]. These methods have many limitations and produce less reliable results, which has already been described in the literature (e.g., [9; 12; 13]). Therefore, for some time, work has been carried out on the possibility of using non-destructive methods in conjunction with artificial intelligence in order to assess the moisture content of brick walls, e.g., the works of [PERSON] et al. [14]. Their studies concerned a new method of testing moisture using electrical tomography and machine learning methods for the spatial analysis of wall moisture, the correctness and usefulness of which was confirmed by in situ tests. A prototype of the hybrid tomograph that was used in this method was constructed, but according to the authors of the indicated works, it is also necessary to conduct further research in order to create the final version of the device--a compact portable variation. Likewise, the studies and analyzes carried out to date by the author of this paper have shown that artificial intelligence can be successfully used to reliably assess the moisture content of brick walls [15; 16]. This is possible on the basis of several set parameters that are obtained during testing with the use of non-destructive methods in combination with machine learning algorithms. The method of such assessment, which was developed and recently published [16], may be particularly applicable in situations where moisture content tests are carried out on historical buildings. An undoubted advantage of this method is the possibility of obtaining reliable quantitative results with the least possible interference in the wall structure and in a large number of measurement points. It can therefore be expected that this method could be widely accepted by conservation services.