## 5 Discussion
### Exploratory Factor Analysis  
In this section, we will explore the feasibility of using EFA and the engineering significance of the results obtained from EFA.  
When it comes to measuring raw data, there are traditionally four levels: nominal, ordinal, interval, and ratio. Ratio data is measured in equal distances with an \"absolute zero point\". It is important to note that analyzing the other three types of data with EFA may have some conceptual limitations, except for ratio data [67]. In theory, EFA is most suitable for analyzing ratio data, such as FOS. In the field of geotechnical engineering, FOS is used to measure slope stability, where a FOS \(<\) 1 indicates that the slope is unstable. The number '1' represents the 'absolute zero point' of stability in slope engineering. Therefore, FOS data fall into the ratio category, making it feasible to use EFA for FOS data analysis.  
The slope can fail in different ways, as outlined in Section 4.2.1. The researchers used three types of weak interlayer simulation methods in the FOS calculations. It is confusing that the FOS of the two failure modes show similar trends. It is hard to determine the order of variable importance directly from the FOS data. EFA provides a preferable solution to primarily elucidate the importance levels of triggering factors of different failure modes. The factor loading reflects the importance of each variable in a common factor. The more important variable has the greater factor load value. We sorted and analyzed the factor load matrix. The aim was to find the variable that has the greatest impact on each common factor.  
Figure 15 compares the position of the sliding surfaces; it is clear that the sliding surface of Mode 1 is relatively shallower than that of Mode 2. That is to say, the influenced scope of Mode 1 is smaller than that of Mode 2. In fact, for a particular slope, although different conditions can induce its instability, the scope of instability depends on the destructive power of the triggering factors. Specifically, the impact of rainfall on the slope is limited, so generally, the failure scope induced by it is relatively small [80]. Unpredictable earthquakes trigger bedding rock landslides that are often characterized by large scales and fast speeds [80]. EFA detects this well in the FOS data. For Mode 1, a local failure mode, EFA identified rainfall as the main triggering factor. For Mode 2, a relatively large-scale failure mode, EFA identified earthquakes as the main triggering factor. The secondary factor of these two failure modes is prestressing loss, which shows that the anchoring structure has a non-negligible effect on slope stability. EFA explains the factors of the failure modes from a statistical point of view, and its results also reflect rationality. Bringing EFA into the process of forward analysis can help designers identify the main triggers and make decisions early. Based on the results in Sections 4.2.1 and 4.3, we provide the following suggestions for future mitigation:  
1. [label=0.,leftmargin=*]
2. Drainage ditches should be added at the slope to moderate the seepage caused by rainfall. The occurrence of cracks should be monitored to deal with the occurrence of local damage (Failure Mode 1).
3. Measures to resist horizontal load should be set to prevent the development of deep sliding surfaces (Failure Mode 2), including, but not limited to, increasing the anchor  
Figure 14: The Y-direction displacement of the sliding surface after failure in Model 1.  
ing depth, improving the material resistance of the anchoring section, and optimizing the inclination of the anchoring structure.