# Dynamic Quality Monitoring System to Assess the Quality of Asphal Concrete Payement
## Example Verification and Model Training  
### Application Case Study  
With the aim of verifying the applicability of the digital construction quality evaluation model for asphalt pavement, this study adopts a digital highway construction project as an illustrative example to evaluate construction quality. In this project, firstly, based on the dynamic monitoring system of asphalt concrete pavement quality established above, the construction quality monitoring equipment is installed on one asphalt mixing plant, 20 asphalt mixture transportation vehicles, five pavers and 10 rollers involved in the project, so as to realize the digital transformation of construction machinery. Secondly, advanced technologies, such as BDS, 5G and the Internet of Things, are used to collect the evaluation index data of the asphalt mixture production, transportation, paving and rolling on the construction site. At the same time, the digital quality evaluation model of asphalt pavement established in this study is used to evaluate the pavement quality. Thirdly, the PCQ proposed in this study is combined with the pavement performance index to verify the effectiveness of the model. To enable scientific evaluation of the quality of pavement digital construction, 20 industry experts were invited to form an expert group, which included project construction participants, company managers and researchers in pavement construction. The 1-9 scaling method was used to score the index. The judgement matrix of the target layer and the criterion layer was established from bottom to top according to the hierarchical structure of the evaluation index system, and the consistency test was carried out according to Formulas (6) and (7). The score of the evaluation index system is shown in Table 2, and the subjective weight of the final index is shown in Table 3. The initial matrix is established according to the PWL score value of each evaluation index, and the objective weights shown in Table 4 were obtained by using the entropy weight method calculation Formulas (10) and (11). Finally, the subjective and objective combination weights were obtained according to Formula (12). The value of each index combination weight is shown in Table 5. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Indexes** & \(\mathbf{w_{i}}^{\prime}\) & **Indexes** & \(\mathbf{w_{i}}^{\prime}\) & **Indexes** & \(\mathbf{w_{i}}^{\prime\prime}\) \\ \hline C1 & 0.9819 & 0.0486 & C7 & 0.9839 & 0.0432 \\ C2 & 0.9751 & 0.0668 & C8 & 0.9674 & 0.0874 \\ C3 & 0.9646 & 0.0948 & C9 & 0.9848 & 0.0408 \\ C4 & 0.9677 & 0.0865 & C10 & 0.9681 & 0.0855 \\ C5 & 0.9705 & 0.0792 & C11 & 0.9355 & 0.1731 \\ C6 & 0.9645 & 0.0952 & C12 & 0.9631 & 0.0989 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Calculation results of objective weight (\(\mathbf{w_{i}}^{\prime\prime}\)) by the entropy weight method. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**A** & **B1** & **B2** & **B3** & **B4** & \(\mathbf{w_{i}}\) \\ \hline B1 & 1 & 3 & 2 & 1 & 0.3584 \\ B2 & 1/3 & 1 & 1/2 & 1/3 & 0.1100 \\ B3 & 1/2 & 2 & 1 & 1 & 0.2301 \\ B4 & 1 & 3 & 1 & 1 & 0.3015 \\ \hline B1 & C1 & C2 & C3 & C4 & \(\mathbf{w_{i}}\) \\ \hline C1 & 1 & 2 & 1 & 1 & 0.2810 \\ C2 & 1/2 & 1 & 1 & 1/3 & 0.1571 \\ C3 & 1 & 1 & 1 & 1 & 0.2428 \\ C4 & 1 & 3 & 1 & 1 & 0.3191 \\ \hline B3 & C6 & C7 & & C8 & \(\mathbf{w_{i}}\) \\ \hline C6 & 1 & 1 & 2 & & 0.4126 \\ C7 & 1 & 1 & 1 & & 0.3275 \\ C8 & 1/2 & 1 & 1 & & 0.2599 \\ \hline B4 & C9 & C10 & C11 & C12 & \(\mathbf{w_{i}}\) \\ \hline C9 & 1 & 1 & 1/2 & 1/2 & 0.1692 \\ C10 & 1 & 1 & 1/2 & 1 & 0.2046 \\ C11 & 2 & 2 & 1 & 1 & 0.3383 \\ C12 & 2 & 1 & 1 & 1 & 0.2879 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Judgement matrix of indicator layer A and indicator layer B.