## 3 Results and Discussion
### Quantitative Analysis of Soil Microstructure  
The existence of pores in the soil structure was one of its important characteristics, and the microscopic characteristics of the pores had a direct impact on the macroscopic engineering properties of the soil. The major factor impacting the structural properties of soft soil following lateral compression was the change in pore characteristics. As a consequence, the changes in pore characteristics as a function of consolidation time were quantitatively analyzed, and the weakening mechanism of the soft soil structure as a result of pile driving is discussed in this study.  
In this study, Image-Pro Plus 6.0 (IPP) was utilized for image processing and quantitative analysis. The purpose of image processing was to extract information from the images and output data of pore characteristics for statistical analysis. The processing steps were divided into three: the calibration of the pixel space, binarization processing and automatic software analysis [37]. (1) Firstly, a calibration of the pixel space was required. The experiment of the field emission scanning electron microscope in this study adopted a magnification of 8000 times, allowing a unified pixel space to be used for calibration. (2) Secondly, when converting a grayscale image to a binary image, the setting of the threshold was critical for SEM image processing. Due to the differences in the light and uniformity of gold plating during the photographing, many particles in the images were present in the pores, not just on the sliced surface of the sample. If the threshold was set too high, these particles were regarded as pores, and if the threshold was set too low, some tiny pores near the soil particles were counted as soil particles. As a result, during the binarization process, a suitable threshold must be selected, as shown in Figure 4. After binarization, the white part of the image represents soil particles, whereas the black part represents pores. The color may also be changed according to the requirements. (3) Finally, after binarization, IPP software could identify, mark and obtain the characteristic parameters of the microstructure, and it could count parameters such as the diameter, area, perimeter, shape factor, roundness, etc., as shown in Figure 5.  
In this section, the following parameters were selected to analyze the changes in pore characteristics:  
Figure 4: Image binarization processing.  
Figure 5: Schematic of selecting measurements.  
1. Diameter \(D\) The diameter is the most basic feature to describe the size of pores. The diameter of the pores is the average value of the length of the line connecting two points on the contour of the selected object and passing through the centroid.
2. Equivalent diameter \(D^{\prime}\) The equivalent diameter is the diameter of the equivalent circle with the same area as the pore, and its calculation formula is: \[D^{{}^{\prime}}=2(A/\pi)^{1/2}\] (1) where \(A\) is the area of the pore.
3. Planar porosity \(P\) Planar porosity is the percentage of the measured area of the pore to the total area of the image, and its calculation formula is: \[P=A/A_{1}\times 100\%\] (2) where \(A_{1}\) is the total area of particles and pores.
4. Circularity \(R_{0}\) \[R_{0}=L^{2}/4\pi A\] (3) where \(L\) is the perimeter of the pore.