# Development Characteristics and Mechanism of Crack in Expansive Soil under Wet-Dry Cycling
## 3 Indoor Wet-Dry Cycle Test of Expansive Soil
### Sample Preparation and Testing Methods
Subsequently, utilize a digital camera to capture photographs of the samples' surface and sides. Then, reassemble the samples within a ring cutter, with permeable paper and a 5 mm-thick permeable stone placed both above and below the samples. Secure the assembly using a G-clamp and subsequently subject it to saturation by placing it in a vacuum saturation device. Finally, after maintaining the samples under constant conditions for 24 h and completing the saturation process, remove them and position them back in the drying oven. Capture additional photographs when the moisture content reaches approximately 12%, marking the beginning of the second dry-wet cycle. Repeat these steps for the third to fifth dry-wet cycles. Ultimately, the diagram depicting the change in crack morphology, as illustrated in Figure 4, is acquired. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample Number**} & **Initial Moisture** & **Compaction** & **Thickness (cm)** & **Expansiveness** & **Number of Dry-Wet Cycles** \\  & **Content** & **Degree (\%)** & **Thickness (cm)** & **Expansiveness** & **Dry-Wet Cycles** \\  & **\(w\) (\%)** & **Degree (\%)** & **Thickness (cm)** & **Expansiveness** & **Number of Dry-Wet Cycles** \\ \hline A-1 & 30, 35, 40 & 80 & 3 & & \\ A-2 & 35 & 80, 90 & 3 & Moderately/Highly & 1, 2, 3, 4, 5 \\ A-3 & 35 & 80 & 1, 2, 3, 4 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Basic information of the dry–wet cycling test samples. Figure 4: Crack morphology variation during wet–dry cycling process. **(3) Crack image processing**  
Digital imaging was employed to quantitatively characterize the development of cracks in expansive soil specimens. The specific procedure is outlined as follows:  
1. Initially, the RGB color image was grayscaled using Photoshop software (V13.0.1.3), with the grayscale value typically set to 0 or 255;
2. Subsequently, the grayscale image was imported into PCAS crack image analysis and processing software for binary image processing (Figure 5c);
3. The denoising function of the PCAS software (V2.3) was then utilized to remove noise from the binary image (Figure 5d);
4. Subsequently, crack skeletonization processing was performed (Figure 5e). The crack ratio represents the aggregate of crack areas per unit area of soil [27], determined through image processing. Specifically, it denotes the ratio of black cracks in binarized images of expansive soil samples to the entire image. The precise calculation formula is presented below:  
\[\delta_{c}=\sum\limits_{i=1}^{n}\frac{A_{i}}{A}=\frac{n_{b}}{n_{b}+n_{w}}= \frac{n_{b}}{n}. \tag{1}\]  
In the equations, \(A_{i}\) and \(A\) denote the area of the \(i\)th crack and the sample area, respectively, while \(n_{b}\) and \(n_{w}\) represent the counts of black and white pixels, and n signifies the total number of pixels. During crack length computation, the software automatically categorizes cracks into three groups--horizontal, vertical, and diagonal lines, with horizontal and vertical lines standardized to a length of 1 unit each, and diagonal lines treated as individual units. Subsequently, upon gathering and categorizing all cracks within the image, the lengths of the three types of cracks are summed to determine the total crack length. The average width of cracks is represented by the ratio of the total crack area to the total crack length. The specific calculation is as follows:  
\[\overline{b}=\frac{\sum\limits_{i=1}^{n}A_{i}}{\sum\limits_{i=1}^{n}l_{i}}. \tag{2}\]  
In the equation above, \(l_{i}\) denotes the length of the \(i\)th crack, while the remaining parameters carry the same significance as in Equation (1). Figure 5: Fracture image processing process.