Estimation of water-to-cement ratio in cementitious materials using electrochemical impedance spectroscopy and artificial neural networks

In this study, electrochemical impedance measurements were investigated to estimate the water-to-cement (w/c) ratio of unhardened cementitious materials. The proposed approach is based on electrochemical impedance spectroscopy (EIS) and artificial neural networks (ANN), where the interpretation of EIS data was significantly improved without subjectively selected equivalent circuit models. The EIS method is sensitive to the changes in the electrochemical properties of a tested material such as the capillary porosity of cement paste, which enables the use of EIS in estimating the w/c ratio of cementitious materials. The EIS data were collected from cement paste samples with a w/c ratio in the range of 0.30-0.45. The following features were considered to train ANNs: raw impedance data (both real and imaginary parts); features extracted by the raw impedance data via the principal component analysis; and circuit parameters extracted from the raw impedance data via analysis of equivalent circuit models suggested from previous studies. Experimental results indicate that the w/c ratio can be predicted from the fresh-state cement composites with a mean absolute error of 0.014 using the ANN trained with principal components. The results demonstrate that the proposed approach significantly improves EIS data interpretation without subjectivity and shows considerable potential for estimating the w/c ratio of cementitious materials in situ.