###### Abstract
A lack of consent of conservator services for taking samples often results in the skipping of the scaling procedure, and instead the results of scaling that were conducted by the apparatus manufacturer are being used. It is usually made on the basis of testing moist masonry samples that contain water without soluble salts, whereas in moist brick masonry, walls of existing objects such salts are present. As was shown in [19],  
Figure 1: Classification of the moisture (**a**) and salinity (**b**) of brick walls (based on [8; 9; 10; 11; 12; 13; 14]). their content significantly affects the results of tests that were obtained using electrical methods and also causes the overstatement of the estimated humidity value when compared to the actual value. In order to eliminate troublesome scaling of the apparatus on the examined object, it was proposed to use the possibilities offered by artificial intelligence. Nowadays, artificial intelligence is extensively used when predicting humidity values in various parts of buildings made of brick. The authors of works [20; 21; 22] recently determined the compressive strength of brick-mortar masonry based on neural networks, neuro-fuzzy inference systems, and nondestructive tests. The authors of work [23] employed support vector machines, neural networks, and Gaussian Naive Bayes techniques for the evaluation of damage in a turn-of-the-century, six-story building with timber frames and masonry walls. The authors of work [24] used mobile deep learning for damage detection of historic masonry buildings. Another attempt to classify the damage of masonry historic structures based on convolutional neural networks and still images was presented in [25]. Convolutional neural networks were also used in [26; 27] for the prediction and metamodeling of the hygothermal behavior and performance of building components. In [28], a method based on numerical experiments for the identification of the thermal resistance of exterior walls of buildings was presented. However, there is still a lack of research that aims to use artificial neural networks and nondestructive methods to assess the humidity of saline brick walls in historic buildings. This is why the authors of this publication, based on experimental research and numerical analysis, have developed a nondestructive neural method of assessing the humidity of saline brick walls in historic buildings, which is described in detail in [29]. This method is based on the use of artificial neural networks that are learned and tested on a data set built for this purpose. However, this method requires validation by verification on other historic buildings. This has not been done before and can be seen to be necessary before implementing it in construction practice. This issue is currently the subject of research, and the purpose of this work is to present the first results of such verification carried out on two historic buildings selected for this purpose. As mentioned before, such verification has not been done to date and is therefore a new contribution to the knowledge related to the issue. Description of the Previously Developed Nondestructive Method for Assessing the Humidity of Saline Brick Walls in Historical Buildings  
As described in [29], data collection was obtained on the basis of research of in-situ brick walls of several selected historic buildings from various historical periods from the 14 th to 19 th centuries. The tests were carried out in several hundred places. The data set, which is fully available in [29], therefore included several hundred sets of test results, each of which consists of six parameters. Two basic dimensionless parameters, \(X_{\text{D}}\) and \(X_{\text{M}}\), describing the moisture of the wall, were determined using nondestructive, dielectric (Gann Hydromette Uni 2 meter with an active ball probe) and microwave (Trotec T 600 meter) methods.