4.2.1. Macropore Morphology

Pore segmentation was performed by subtracting images before and after staining, followed by global thresholding. The particular technique was described in more detail in previous work [12]. The segmentation result, i.e., the binary image of pores, is shown in Figure 10. Figure 11 shows a contour map of pore local thickness, as well as its histogram. Graphs of porosity and average pore thickness as a function of coordinate *R* are presented for the consideration sample in Figure 12. The brightest gray lines correspond to the width Δ *R* (as in Figure 4) equal to the pixel size. Darker lines correspond to greater values of adopted width Δ *R*. The same remark holds true throughout the remaining graphs in the paper.

**Figure 7.** Scan of tested sample after staining the pores.

**Figure 8.** Scan of the tested sample after staining the matrix.

**Figure 9.** Image of the tested sample after staining the matrix (limited to the region of interest (ROI)).

**Figure 10.** Binary image of pores (limited to ROI).

(**a**) 

**Figure 11.** Pore local thickness: (**a**) contour map; (**b**) histogram.

**Figure 12.** Porosity (**a**) and average local thickness of pores (**b**) as a function of *R* coordinate.

### 4.2.2. Cement Matrix Morphology

The matrix was segmented by extracting the appropriate color channels from the scan after staining the matrix, followed by thresholding. Next, the pores were removed and the calculations were carried out. Figure 13 presents a map of the local thickness of the cement matrix together with the histogram. Figure 14 shows the variability of the volume fraction and the average local thickness of the matrix as a function of coordinate *R*.

### 4.2.3. Aggregate Morphology

The aggregate was segmented as a complement of the area, being the union of the binary images of cement matrix and pores. The map of local aggregate thickness, as well as its histogram, is presented in Figure 15. Figure 16 shows the variability of the volume fraction and average aggregate thickness as a function of coordinate *R*.

### *4.3. 3D Imaging in Microcomputed Tomography*

In order to describe the pore space network, a nondestructive technique, namely, μCT, was used. The sample was cut to a cuboid with dimensions allowing to obtain a test resolution of 21.40 μm/pix. Then, the prepared cuboid was fixed to the holder in the chamber of the device (Figure 17).

(**a**)

(**b**) 

**Figure 13.** Local thickness of cement matrix: (**a**) contour map (limited to ROI); (**b**) histogram.

**Figure 14.** Volume fraction (**a**) and average local thickness (**b**) of cement matrix as a function of the element thickness.

**Figure 15.** Local thickness of aggregate: (**a**) contour map (limited to ROI); (**b**) histogram.

**Figure 16.** Volume fraction (**a**) and average aggregate thickness (**b**) as a function of coordinate *R*.

**Figure 17.** The sample attached to the holder and mounted in the chamber of X-ray scanner.

Scanning was performed in a Bruker Skyscan 1172 device (Bruker, Kontich, Belgium). The examination consisted of acquiring a series of X-ray projections, followed by the reconstruction of 3D image of the tested sample. The scanning parameters used are shown in Table 3.


**Table 3.** Selected, more important scanning parameters.

Image reconstructions were made using the NRecon program based on the Feldkamp algorithm [25]. The set of reconstruction parameters is presented in Table 4.


The reconstructed structure of the tested sample is shown in Figure 18a. In order to carry out a quantitative and qualitative assessment of the material, it was also necessary to specify in the reconstructed model the volume of interest (VOI). A quasi-cuboidal area was assumed, highlighted in green in Figure 18b.

**Figure 18.** Reconstruction of the tested sample: (**a**) rendered three-dimensional (3D) view with exemplary cross-sections; (**b**) volume of interest (VOI) selection.

Macropore Morphology

The first stage of the analysis was pore segmentation using the threshold method preceded by the use of a smoothing filter. The spatial morphology of the pores extracted in this way is shown in the perspective view in Figure 19a, in which the pores are indicated in red and the reconstruction is shown in gray, for which a high level of transparency was set. Morphometric analysis was performed on the binary image of the pore space. In particular, porosity and local pore thickness were determined. The variability of these quantities as a function of coordinate *R* is shown in Figure 19b,c. Figure 19d presents the histogram of pore size. The variation with *R* was determined in a manner analogous to that used in 2D analyses. This time, however, subsequent layers (horizontal sections) of the 3D image were treated as circumferential bands.

**Figure 19.** Results of morphometric analysis: (**a**) segmentation of pore space; (**b**) macroporosity as a function of coordinate *R*; (**c**) average local pore thickness as a function of coordinate *R*; (**d**) histogram of pore local thickness.

### **5. Test Results Obtained Using Di**ff**erent Methods**
