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- W4324361454 abstract "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." @default.
- W4324361454 created "2023-03-16" @default.
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- W4324361454 date "2023-04-01" @default.
- W4324361454 modified "2023-10-16" @default.
- W4324361454 title "Water absorption prediction of nanopolymer hydrophobized concrete surface using texture analysis and machine learning algorithms" @default.
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- W4324361454 doi "https://doi.org/10.1016/j.conbuildmat.2023.130969" @default.
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