## 3 Results and Discussion
### Optimization with Multiple Response Performance Index (MRPI)  
Only one dependent variable, response, was evaluated in Taguchi optimization, and according to S/N performance statistics, the optimum levels were found for penetration and softening point before and after aging, RTFOT mass loss, elastic recovery, and Marshall stability. In real-life problems, it is common to have more than one dependent variable, just like in our study. However, the Taguchi method focuses on single-response scenarios, which makes it unsuitable for optimizing multi-response scenarios directly. Nonetheless, the data obtained for each response using Taguchi designs can be analyzed using different approaches developed by various researchers [107]. [PERSON] et al. [108] conducted a literature review discussing the application of the Taguchi method for solving multi-response issues. In this study, the assignment of weights method was used to transform multiple responses into one [109]. In the Taguchi Method, the S/N values obtained for each test type using the \"larger is better\" performance statistic were divided by the sum of S/N values. The reverse normalization procedure was used for S/N values calculated using the \"smaller is better\" performance statistic. The maximum MRPI values were used to decide on the optimum levels since MRPI is a score that considers different factors. Table 17 shows the weights and MRPI values obtained from S/N. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & & & & & & & \\ \cline{2-10}
**Experiment** & **Penetration** & **Penetration** & **Softening** & **Softening** & **RTFOT** & **Class Loss** & **Class** & **Marshall** & **MRPI** \\
**No** & **Before** & **After** & **Before** & **Pattern After** & **RTFOT** & **RTFOT** & **Mass Loss** & **Recovery** & **Stability** & **MRPI** \\ \hline
1 & 0.062 & 0.061 & 0.062 & 0.062 & 0.061 & 0.038 & 0.063 & 1.870 \\
2 & 0.062 & 0.061 & 0.062 & 0.062 & 0.061 & 0.041 & 0.061 & 1.773 \\
3 &  
By converting the responses obtained from all experiments into a single response, the optimum levels for the MRPI results were found to be A3 (0.4%), B4 (140 \({}^{\circ}\)C), C4 (30 min), and D1 (1000 rpm). When the mixing temperature in a natural gas asphalt plant decreases by 17 \({}^{\circ}\)C, greenhouse gas (GHG) emissions are reduced by 9% [110]. Therefore, the 20 \({}^{\circ}\)C reduction in the optimized mixing temperature not only contributes to sustainable pavement construction, but also leads to lower greenhouse gas emissions. The parameters that had the highest impact on the multiple response performance indices were Cecabase RT BIO10 rate, time, temperature, and speed, respectively. Verification test results and performance statistics for MRPI optimum levels are presented in Table 18. The verification test results for MRPI were found to be within the calculated limits within the 95% confidence interval. With a 95% confidence interval, 256 \(\times\) 7 = 1792 estimations were conducted, and the distribution chart is given in Figure 13. By using the radar chart, it can be seen in advance which result will be obtained at the desired parameter levels for each type of experiment, whether it is in the experimental plan or not. For example, higher penetration (soft) bitumen is preferred in cold climates to prevent asphalt from becoming brittle and cracking at low temperatures. Lower penetration (stiff) bitumen is desired in hot climates to prevent asphalt from softening and rutting. However, other features can be ignored when choosing the penetration appropriate to the conditions. Therefore, while selecting the penetration appropriate to the climate conditions according to the data in the graph, it is also possible to choose which levels should be assigned to optimize other features, such as stability.