Non-destructive neural identification of the moisture content of saline ceramic bricks

•The identification of the moisture content of saline ceramic bricks was presented.•The bricks were placed in aqueous nitrate, chloride and sulphate environments.•Neural identification of the moisture content of saline ceramic bricks is possible.•Broyden-Fletcher-Goldfarb-Shanno (BFGs) learning algorithm was applied.•Presented approach can be especially useful in historical buildings.

The use of the artificial neural network (ANN) for the non-destructive identification of the moisture content of saline ceramic bricks is presented in the following article. A database was built based on conducted laboratory tests of solid ceramic bricks using dielectric, resistive and microwave methods. The database consists of parameters determined with the above-mentioned methods and also parameters describing the type and concentration of salts in the bricks. As a result of carried out research and analysis it was proved that neural identification of the moisture content of saline ceramic bricks is possible.