Moisture Distribution in Partially Saturated Concrete Studied by Impedance Spectroscopy

The moisture content and its spatial distribution has a great influence on the durability properties of concrete structures. Several non-destructive techniques have been used for the determination of the total water content, but moisture distribution is difficult to determine. In this paper impedance spectroscopy is used to study the water distribution in concrete samples with controlled and homogeneously distributed moisture contents. The technique is suitable for the determination of water distribution inside the sample, using the appropriate equivalent circuits. It is shown that using the selected drying procedures there is no change in the solid phase of the samples, although the technique can only be used for the qualitative study of variations in the solid phase when samples are too thick. The results of this work show that for a wide range of concrete percentages of saturation, from full to 18 % saturation, practically all the pores keep at least a thin layer of electrolyte covering their walls, since the capacitance measurement results are practically independent of the saturation degree.

An adaptive algorithm for clustering cumulative probability distribution functions using the Kolmogorov–Smirnov two-sample test

This paper proposes an adaptive algorithm for clustering cumulative probability distribution functions (c.p.d.f.) of a continuous random variable, observed in different populations, into the minimum homogeneous clusters, making no parametric assumptions about the c.p.d.f.’s. The distance function for clustering c.p.d.f.’s that is proposed is based on the Kolmogorov–Smirnov two sample statistic. This test is able to detect differences in position, dispersion or shape of the c.p.d.f.’s. In our context, this statistic allows us to cluster the recorded data with a homogeneity criterion based on the whole distribution of each data set, and to decide whether it is necessary to add more clusters or not. In this sense, the proposed algorithm is adaptive as it automatically increases the number of clusters only as necessary; therefore, there is no need to fix in advance the number of clusters. The output of the algorithm are the common c.p.d.f. of all observed data in the cluster (the centroid) and, for each cluster, the Kolmogorov–Smirnov statistic between the centroid and the most distant c.p.d.f. The proposed algorithm has been used for a large data set of solar global irradiation spectra distributions. The results obtained enable to reduce all the information of more than 270,000 c.p.d.f.’s in only 6 different clusters that correspond to 6 different c.p.d.f.’s.