## 3 Results and Discussions
### Material Properties  
A standardized 2D SEM image technique was implemented to measure the pore size distribution ([PERSON] et al., 2017) and pore characteristics of materials ([PERSON] et al., 2017; [PERSON] et al., 2017). The 2D morphological properties of concrete were determined using 2D projections. 3D morphological properties were obtained using reconstructed multiphysics models. The SEM images were captured using a JOEL scanning electron microscope (JSM-IT800). The image processing technique was adapted to analyze the percentages of air gaps, cement matrix, and aggregate distribution in the concrete. A series of 2D X-ray images are used to create 2D and 3D multiphysics model images. Images in 2D and 3D can be utilized to analyze a specimen's interior structure ([PERSON] et al., 2017). Figure 6: Split tensile strength of concrete with different proportions of NS and RHA. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No. of Sample**} & \multirow{2}{*}{**NS and RHA in Concrete (\%)**} & \multicolumn{3}{c}{**Compressive Strength (N/mm\({}^{2}\))**} \\ \cline{3-5}  & & **7 Days** & **14 Days** & **28 Days** \\ \hline
1 & 0\% & 1.98 & 2.32 & 2.56 \\
2 & 0.5\% NS \& 5\% RHA & 2.16 & 2.50 & 2.73 \\
3 & 1.0\% NS \& 10\% RHA & 2.70 & 2.96 & 3.28 \\
4 & 1.5\% NS \& 15\% RHA & 2.34 & 2.71 & 3.07 \\
5 & 2.0\% NS \& 20\% RHA & 2.03 & 2.35 & 2.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Split tensile strength of concrete. The specimen is rotated between an X-ray source and an X-ray detector on a rotating table. For each location of the revolving table, X-rays are transmitted through the specimen. Each slice gets 2D X-ray transmission images as a result of this. Special algorithms are used to rebuild the images of the slices, resulting in a 3D data set for the specimen structure. A deep mapping algorithm from MATLAB image processing toolbox was adopted. Image processing is a technique for implementing operations on an image in order to improve it or extract valuable information from it. It is a form of signal processing in which the input is an image, and the output is either that picture or its characteristics/features. Image processing converts an image into digital form and applies a procedure to it to obtain a better image or extract useful information. The first one is the enhancement of visual data for human interpretation, and the second one is scene data processing for autonomous machine perception. Such examination is one of the most popular non-destructive techniques to describe concrete microstructure without damaging the material. The first image on the top left position of Figures 7-9 show an original 2D SEM image [46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56] which allows the user to generate a binary image shown immediately on the right. This binary image represents the pore phase in the target component of this investigation. One of the main novelties of this study is the quantitative use of SEM images to detect and measure the size of concrete pore spaces using a watershed segmentation algorithm. The optimum dosage of NS 1.0% and RHA 10% [57; 58] was chosen for this image processing. Figure 7: _Cont._  
The binary image consists of clustered pores that should be separated from the sample. A modified deep mapping algorithm was used to identify the clear pore segments. The next image on the right shows different colors denoting every single pore which generates the next pore size segmental image on the right. The pore space segmentation characterization is an important method to understand the mechanical properties of concrete [35; 59; 60; 61; 62; 63]. Table 8 shows the output of MATLAB Analysis of different images of NS and RHA. Image processing is a non-destructive technique that can be used for predicting the compressive strength of concrete with various applications in the construction sector. The  
Figure 7: 2D SEM images of concrete microstructures. SEM image technique was used to analyze the concrete samples' binary segmentation and pore space segmentation.