## 3 Case Study
### Discussion  
The best way to verify the validity of the model is to compare its accuracy and precision with that of previous studies. However, engineering practice usually uses qualitative analysis methods; there are few quantitative research models at present. Of those available, the grey correlation analysis (GRA) construction sequence model [9] with fixed attributes may not be universally applicable, and the construction schedule optimization model [10] focuses on the total discount cost factor, which is inconsistent with the scope of this study. Thus, they are not suitable for use in a comparative analysis with our proposed model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Line**} & \multicolumn{6}{c}{**Indicator**} \\ \cline{2-7}  & \(C_{1}\) & \(C_{2}\) & \(C_{3}\) & \(C_{4}\) & \(C_{5}\) & \(C_{6}\) & \(C_{7}\) \\ \hline Line 1 & 0.056518 & 0.014200 & 0.047416 & 0.162573 & 0.130466 & 0.060530 & 0.085790 \\ Line 2 & 0.032764 & 0.009939 & 0.051168 & 0.186051 & 0.032616 & 0.049437 & 0.090079 \\ Line 3 & 0.060204 & 0.028272 & 0.058673 & 0.221139 & 0.043489 & 0.046141 & 0.079061 \\ Line 4 & 0.033583 & 0.016002 & 0.045369 & 0.067455 & 0.054361 & 0.035873 & 0.067603 \\ Line 5 & 0.093377 & 0.031164 & 0.089033 & 0.181399 & 0.000000 & 0.060128 & 0.060436 \\ Line 6 & 0.082320 & 0.044954 & 0.082551 & 0.215641 & 0.010872 & 0.019493 & 0.072636 \\ Line 7 & 0.147438 & 0.017586 & 0.034112 & 0.030814 & 0.250060 & 0.112545 & 0.132170 \\ Line 8 & 0.055289 & 0.020378 & 0.046051 & 0.040522 & 0.000000 & 0.052496 & 0.076736 \\ Line 9 & 0.043412 & 0.030205 & 0.064472 & 0.007731 & 0.054361 & 0.115207 & 0.065091 \\ Line 10 & 0.018839 & 0.019436 & 0.033771 & 0.021951 & 0.119594 & 0.037395 & 0.016284 \\ Line 11 & 0.002457 & 0.000000 & 0.009551 & 0.146247 & 0.000000 & 0.055494 & 0.095867 \\ Line 12 & 0.000000 & 0.009406 & 0.000000 & 0.144128 & 0.000000 & 0.003087 & 0.071406 \\ Line 13 & 0.004915 & 0.031731 & 0.048780 & 0.000000 & 0.065233 & 0.047692 & 0.009523 \\ Line 14 & 0.001229 & 0.023343 & 0.043322 & 0.019371 & 0.021744 & 0.036367 & 0.000000 \\ Line 15 & 0.029078 & 0.009824 & 0.020126 & 0.061276 & 0.021744 & 0.000000 & 0.033854 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Weighted decision matrix for the construction sequence of Line 1-15.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Line** & **Construction Sequence** & **Construction Timing** \\ \hline Line 7 & 1 & November 2005 \\ Line 1 & 2 & July 2009 \\ Line 2 & 3 & December 2010 \\ Line 3 & 4 & April 2012 \\ Line 5 & 5 & June 2013 \\ Line 6 & 6 & August 2014 \\ Line 4 & 7 & June 2015 \\ Line 11 & 8 & June 2015 \\ Line 12 & 9 & June 2016 \\ Line 9 & 10 & February 2017 \\ Line 10 & 11 & August 2017 \\ Line 8 & 12 & March 2017 \\ Line 15 & 13 & June 2018 \\ Line 13 & 14 & February 2018 \\ Line 14 & 15 & September 2019 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Calculation results of construction schedule of lines 1-15.  
Instead, we will verify the validity of the model by comparing the model calculation results with the actual construction schedule.  
Since the operating lines have been tested in practice and have an objective evaluation on their construction sequence, the lines that have been put into operation (Line 1, Line 2, Line 3, Line 4, Line 5, Line 7, and Line 10) are selected for comparative analysis in the study.