Automated measurement system for detecting carbonation depth: Image-processing based technique applied to concrete sprayed with phenolphthalein

This paper aims at discussing an automated measurement system for detecting carbonation depth in concrete
sprayed with phenolphthalein. Image processing and Convolutional Neural Networks strategies are exploited
to accurately separate the carbonated and non-carbonated areas and to remove those aggregates on the
carbonation front that could bring to a wrong evaluation of the carbonation depth. Very strong correlation (R2
> 0.98) is found between results provided by the proposed approach and the method suggested by the EN
13295 standard. The expanded uncertainty (coverage factor k =2) of this novel approach is 0.08 mm. ANOVA
analysis performed in multi-operator tests proved that the highest source of uncertainty is the measurement
system, which, on the other hand, is robust to changes in the operator performing the measurement.