# Applicable Conditions of Room-and-Pillar Mining Goal Treatment Methods under a Traffic Load
## 2 Stability Analysis of a Goaf under Traffic Load Conditions
### Analysis of Traffic Load Disturbance Depth
#### 2.1.2 Orthogonal Design of the Traffic Load Disturbance Depth Parameter and Its Calculation Results  
Orthogonal design is the most important method in a multifactor simulation experiment. This method is a multi-factor simulation test method [29] that is based on the fractional principle of factor design. It uses an orthogonal table derived from combination theory to arrange and design simulation tests. The results are then statistically analyzed. In this orthogonal design, we selected five influencing factors as the study objects. Each factor had seven levels, presented in Table 2. The factors refer to the items that affected the simulation test results, and the levels refer to the size and level of each factor in the simulation test process. According to the principle of orthogonal design, we used the L49(75) orthogonal table to arrange the orthogonal simulation test of five factors and seven levels. Then, we obtained an orthogonal design table for the levels of traffic load disturbance depth parameter factors, presented in Table 3. Table 3 shows that we obtained 49 groups of different levels of combinations of traffic load disturbance depth parameter factors, corresponding to 49 rows in the orthogonal table; that is, the number of simulation tests was 49. We calculated each simulation of the disturbance depth according to a set of parameter levels presented in Table 3, and the calculated disturbance depth was recorded. Following 49 simulations, the results of the traffic load disturbance depth under different combinations of influencing factors and parameters are presented in Table 3. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Thickness** & **Modulus of** & **Poisson’s** & **Unit Weight** & **Internal Friction** & **Cohesion** \\  & **(m)** & **Elasticity (MPa)** & **Ratio** & **(kN/m3)** & **Angle (\({}^{\circ}\))** & **(kPa)** \\ \hline Surface layer & 0.2 & 4000–7000 & 0.25 & 24 & & \\ Base layer & 0.4 & 12,000 & 0.25 & 23 & & \\ Subgrade & 1–7 & 80 & 0.28 & 19 & 28 & 15 \\ Foundation & 70 & 35 & 0.3 & 18–24 & 20 & 11 \\ \hline \end{tabular} Note: (1) The surface layer is asphalt pavement, and the range of elastic modulus was determined by referring to the existing research [24,25] and specifications [26]. The rest of the parameters were determined according to engineering experience. (2) The base layer was a semi-rigid base, and the material was cement-stabilized macadam. The elastic modulus was determined according to the existing research [27] and specification [26], and other parameters were determined according to engineering experience. (3) Parameters of subgrade materials were determined according to engineering experience. (4) The parameters of foundation materials were determined according to engineering experience, in which the unit weight is the average value of the unit weight of each layer of rock and soil within the disturbed depth range.  
\end{table} 
Table 1: Parameters of materials. Figure 3: Schematic diagram of traffic load disturbance depth. It can be observed from Table 4 that the R2 between the dependent and independent variables is 0.932, which indicates that the five influencing factors selected in this study have a strong correlation with the depth of the traffic load disturbance. The adjusted R2 is 0.925, which indicates that the five influencing factors selected in this study can effectively express the change in the traffic load disturbance depth. Further analysis of the significance of the influencing factors shows that when the significance level of 0.05 is adopted, it can be observed from the significance of each factor in Table 5 that the subgrade height, pavement stiffness, vehicle speed, and vehicle load are the significant influencing factors, while the significance of the foundation soil unit weight is 0.272, meaning it is a non-significant influencing factor. Therefore, in the multiple linear regression fitting, the factor of foundation soil unit weight should not be included.