# Effect of Mixture Design Parameters of Stone Mastic Asphalt Pavement on Its Skid Resistance
## 3 Results  
SI was used to evaluate skid resistance. As mentioned before, \(k_{i}\) represents mean values of SI for each factor at a certain level \(i\) (\(i\) = 1, 2, 3, 4), and \(R\) reflects the effect order of different factors. The above three parameters could be calculated by the corresponding formula. Tables 10-13 show the calculation results.  
Tables 10-13 show the skid resistance of SMA-16, SMA-13, SMA-10, and SMA-5 respectively. The following conclusions can be drawn from these tables.  
1. For different SMA types, the effect order of the three factors (P\({}_{\text{NMSA}}\), \(P_{\text{CS}}\), and AC) is constant. Among them, P\({}_{\text{NMSA}}\) has the greatest impact on the skid resistance, AC is the second, and the impact of P\({}_{\text{CS}}\) on skid resistance is the smallest. Moreover, the three parameters have higher impact on skid resistance than the other two parameters in general. It is obvious that the three parameters are the key parameters to improve skid resistance.
2. The effect order of P\({}_{\text{NMSA-1}}\) and P\({}_{\text{CS-1}}\) with different SMA types is various. For SMA-16, P\({}_{\text{NMSA-1}}\) has a greater impact on skid resistance than P\({}_{\text{CS-1}}\), but the conclusion is the opposite for SMA-13.
3. For P\({}_{\text{NMSA-1}}\), SMA pavements with different mixture types have the best skid resistance at the level (\(i\) = 1). For other design parameters, the corresponding levels (\(i\)) with different mixture types are significantly different when the skid resistance is best.
4. Taking SMA-13 as an example, AC (5.6%) is lower than OAC (6.3%) when the skid resistance is best. It is possible that the surface texture of SMA pavement with lower AC is richer, resulting in better skid resistance. It is noticed that the pavement may have excellent skid resistance but poor pavement performance when AC is 5.6%. Therefore, the determination of the best design parameters should be considered both skid resistance and pavement performance.  
Figure 1: The 3D models of pavement texture: (**a**) the model before filtering; (**b**) the model after filtering.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Experiment** & **A** & **B** & **C** & **D** & **E** & **SI** \\ \cline{2-6}
**Number** & \(\mathbf{P_{NMSA}/\%}\) & \(\mathbf{P_{NMSA-I}/\%}\) & \(\mathbf{P_{CS}/\%}\) & \(\mathbf{P_{CS-I}/\%}\) & **AC/\%** & \\ \hline
**1** & 1(90) & 1(65) & 1(20) & 1(15) & 1(5.6) & 4.455 \\ \hline
2 & 1 & 2(71) & 2(24) & 2(18) & 2(5.9) & 3.932 \\
3 & 1 & 3(78) & 3(28) & 3(21) & 3(6.2) & 4.258 \\
4 & 1 & 4(85) & 4(32) & 4(24) & 4(6.5) & 3.855 \\
5 & 2(93) & 1 & 2 & 3 & 4 & 1.294 \\
6 & 2 & 2 & 1 & 4 & 3 & 4.554 \\
7 & 2 & 3 & 4 & 1 & 2 & 3.728 \\
8 & 2 & 4 & 3 & 2 & 1 & 4.097 \\
9 & 3(96) & 1 & 3 & 4 & 2 & 3.872 \\
10 & 3 & 2 & 4 & 3 & 1 & 3.537 \\
11 & 3 & 3 & 1 & 2 & 4 & 2.353 \\
12 & 3 & 4 & 2 & 1 & 3 & 2.714 \\
13 & 4(100) & 1 & 4 & 2 & 3 & 2.087 \\
14 & 4 & 2 & 3 & 1 & 4 & 1.461 \\
15 & 4 & 3 & 2 & 4 & 1 & 2.196 \\
16 & 4 & 4 & 1 & 3 & 2 & 1.810 \\ k\({}_{1}\) & 4.13 & 2.93 & 3.29 & 3.12 & 3.57 & \\ k\({}_{2}\) & 3.42 & 3.37 & 2.53 & 3.12 & 3.34 & \\ k\({}_{3}\) & 3.12 & 3.13 & 3.42 & 3.03 & 3.40 & \\ k\({}_{4}\) & 1.89 & 3.12 & 3.30 & 3.31 & 2.24 & \\ R & 2.24 & 0.44 & 0.89 & 0.28 & 1.33 & \\ Effect order & & & & & & \\ \multicolumn{6}{c}{\(\mathbf{P_{NMSA}>AC>P_{CS}>P_{NMSA-I}>P_{CS-I}}\)} \\ Optimum group & \multicolumn{2}{c}{A1 + B2 + C3 + D4 + E1/P_{NMSA}(90) + P_{NMSA-I}(71) + P_{CS}(28) + P_{CS-I}(24) + AC(5.6)} \\ \hline \hline \end{tabular}
\end{table}
Table 11: The skid resistance of SMA-13.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Experiment** & **A** & **B** & **C** & **D** & **E** & **SI** \\ \cline{2-6}
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