## 3 Results  
Figures 3-5 show the moisture-conductivity relationship for each of the five sized fractions as well as whole samples for NQ, RQ, and RG. The plots are presented with a logarithmic scale in the vertical axis for better appreciation. The volume of data secured that all of the branches were determined by the linear regression of more than six data pairs. The two branches are easily distinguished for each sample. A good correlation between conductivity and moisture was obtained for both the dry and wet branches of all of the samples. The frequency histograms for the R\({}^{2}\) values of each branch and cumulative values are shown in Figure 6. A very good correlation was obtained in almost all branches. This reflects a significant description of the variation in the condition of free water in the sample, depending on the moisture content in relation with the internal porosity of the particles. The intersection of both branches defines the moisture content corresponding to the saturated-surface dry condition, i.e., the water absorption capacity of the granular skeleton. When the moisture content is lower than this intersection value, an insufficient volume of water remains on the surface of the particles to build up the menisci that favor electrical conductivity. However, as moisture content increases, the increase in conductivity is very fast. With moisture contents above the intersection value, larger bridges of water are available to conduct electrical current. As the volume increases proportionally to the increase in the volume of free water, the evolution of conductivity is not as fast as for the other branch. Table 2 presents a summary of the obtained results. The range of variation in the absorption values of the differently sized fractions, and in the whole-size range of NQ, was (3.2-3.8)%, while the standard deviation was 0.30. The corresponding range of values for RQ was (6.2-6.4)% if the value for the size range 0.3-0.15 of 9.2% was excluded, which is around 3 percentage points higher than the rest of the fractions. A similar situation can be described for RG with the range of values (7.7-8.6)%, and an exceptionally high value of 10.8% for the size range 0.3-0.15. The increase in the WA value for the finest sized fraction of RA was also reported by [28] and associated with its high paste content. The increase in paste content with fineness can be explained by the difference in hardness between natural aggregates and cement paste. When concrete is crushed, the cement paste is released in finer particles more easily than natural aggregate [50]. Thus, the concentration of cement paste in the finest fraction is natural. The lack of significant variation in the other sized fractions of the recycled aggregates, and the homogeneous values for all size ranges of NQ, indicate that the WA results mostly reflect the proportion of cement paste present in each sized fraction rather than an effect of the particle size range. The weighted averages of WA values (considering the proportion of each sized fraction in the whole sample) correlate well with the experimental measurements of the whole sample. This confirms the consistency of the results. For the weighted average computations, the WA of the fraction under 0.15 mm was considered equal to that of the 0.3-0.15 fraction, but, given its small proportion, any effect of this assumption on the weighted average value would be very small. Figure 3: Moisture contents versus conductivity plots, including the SSD state of NQ samples. Figure 4: Moisture contents versus conductivity plots, including the SSD state of RQ samples. Figure 5: Moisture contents versus conductivity plots, including the SSD state of RG samples. Table 3 displays the slope between the branches of the variously sized fractions of all aggregates, along with the standard deviations. Because many more menisci should form in wet samples with finer particle sizes, more significant changes in the finest fractions' slope may be expected [47].