## 4 Engineering Application  
In this paper, the loess slope on the south side of Jing Mountain in Lintai County, Gansu Province [20] is taken as the study area, and the stability evaluation indexes are selected for this slope. The area belongs to the Longdong loess hilly geomorphological area, dominated by natural loess gullies and artificially modified steep slopes. Regional tectonics are relatively stable, but it is strongly influenced by external dynamic geological effects such as weathering, gravity, surface water scouring, and groundwater activities, superimposed on the developed surface water system, high annual rainfall, and frequent human engineering activities, resulting in a significant risk of geological hazards. Several unstable loess slopes in the study area are near residential areas and public facilities, posing a high potential threat. Details are shown in Figures 7 and 8. A preliminary construction of a loess slope stability evaluation index system that multi-dimensional indexes of loess stratification and structure, hydrogeological conditions, soil mechanical parameters, and human interference, was conducted in accordance with the principle of primary selection of evaluation indicators. Based on the four basic principles of uniqueness, purposefulness, feasibility, and observability, the qualitative screening of a number of indicators in the primary selection was carried out. Ultimately, as Table 1 illustrates, a simplified assessment framework comprising 22 fundamental indicators was developed. In order to extract the primary influencing elements from the subjectively screened indicators, the assessment indicators were quantitatively screened using the enhanced grey correlation model. Five specialists who are knowledgeable about the geological state of loess slopes were gathered using the Delphi technique to weigh each assessment index in accordance with Table 2 and create the index weight matrix D of Equation (14). The primary  
Figure 8: The landslide’s location in relation to the building [20]. Figure 7: Studying the location of the area and the building [20]. elements impacting the stability of loess slopes were determined by calculating and ranking the weights of the assessment indicators. \[D=\left[ \begin{array}{ccccc}0.08&0.08&0.06&0.05&0.05\\ 0.03&0.03&0.025&0.02&0.035\\ 0.01&0.015&0.015&0.01&0.02\\ 0.12&0.12&0.10&0.11&0.10\\ 0.02&0.025&0.025&0.02&0.03\\ 0.09&0.09&0.08&0.07&0.07\\ 0.025&0.03&0.03&0.02&0.025\\ 0.14&0.14&0.12&0.15&0.13\\ 0.13&0.13&0.12&0.11&0.12\\ 0.25&0.20&0.25&0.18&0.15\\ 0.10&0.10&0.09&0.12&0.11\\ 0.01&0.015&0.015&0.01&0.015\\ 0.015&0.015&0.02&0.015&0.02\\ 0.02&0.02&0.01&0.02&0.015\\ 0.03&0.035&0.035&0.03&0.04\\ 0.02&0.02&0.015&0.02&0.025\\ 0.11&0.11&0.10&0.12&0.10\\ 0.025&0.025&0.03&0.02&0.03\\ 0.03&0.03&0.02&0.025&0.02\\ 0.14&0.14&0.12&0.15&0.13\\ 0.015&0.02&0.02&0.015&0.015\\ 0.20&0.18&0.20&0.24&0.18\\ \end{array} \right] \tag{14}\]  
The reference vector \(D_{0}\) is constructed by extracting the maximum value of each column of the matrix \(D\), as shown in Equation (15). \[D_{0}=\left(0.25,\ \ 0.20,\ \ 0.25,\ \ 0.24,\ \ 0.18\right) \tag{15}\]  
Table 3 displays the weights of the assessment indicators following qualitative screening, which were calculated by combining Equations (3)-(5). Analysis of the weight distribution in Table 2 reveals that the key influencing factors of loess slope stability (screening threshold of 0.0450) can be summarized into the following 10 core parameters: Rock Mass Structure (Indicator 1), Slope Height (Indicator 4), Underground Water Body (Indicator 6), Internal Friction Angle (Indicator 8), Cohesion (Indicator 9), Maximum Monthly Rainfall (Indicator 10), Slope Gradient (Indicator 11), Seismic Intensity (Indicator 17), Hydrological Conditions (Indicator 20), and Anthropogenic Engineering Activities (Indicator 22). Details are shown in Table 3. The Maximum Monthly Rainfall (indicator 10) and Anthropogenic Engineering Activities (indicator 22) have the highest weight among them.