Water absorption prediction of nanopolymer hydrophobized concrete surface using texture analysis and machine learning algorithms

The work concerned the study of surface hydrophobized concrete's physical and mechanical properties. An aqueous emulsion based on nano-silicates (A1) and an oligomeric propylsilicate/silicate (A2) concentrate in three dilution states (100%, 70%, and 50%) were used as surface modification agents. The scanning electron microscopy (SEM) images determined three classes of water absorption (WA). A predictive modeling process was performed to automatically identify 1 of the 3 water absorption classes. For the best model, a classification accuracy of 96% was obtained. After 14 days of testing, the hydrophobization efficiency was still high, over 54% for A1 and 45% for A2 for 100% concentration. The samples achieved the best frost resistance with agents A1 and A2 in a 70% concentration. Experimental studies have confirmed the close relationship between concretes' water absorptivity and their surfaces' SEM images. No similar studies of this type are known.