# Dynamic Quality Monitoring System to Assess the Quality of Asphal Concrete Payement
## Example Verification and Model Training
### Application Case Study
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Indexes** & \(\mathbf{w_{i}}^{\prime}\) & **Indexes** & \(\mathbf{w_{i}}^{\prime}\) & **Indexes** & \(\mathbf{w_{i}}^{\prime}\) \\ \hline C1 & 0.1008 & C5 & 0.1100 & C9 & 0.0510 \\ C2 & 0.0576 & C6 & 0.0943 & C10 & 0.0600 \\ C3 & 0.0864 & C7 & 0.0759 & C11 & 0.1020 \\ C4 & 0.1152 & C8 & 0.0598 & C12 & 0.0870 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Calculation results of subjective weight (\(\mathbf{w_{i}}^{\prime}\)) of AHP. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Indexes** & \(\mathbf{Ej}\) & \(\mathbf{w_{i}}^{\prime\prime}\) & **Indexes** & \(\mathbf{Ej}\) & \(\mathbf{w_{i}}^{\prime\prime}\) \\ \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 & 0.0 used as the input layer and the comprehensive index PCQ is used as the output layer. The relevant parameter settings of the PSO algorithm are shown in Table 6. The final model parameters are determined by comparing the MSE. Using the trial-and-error method, when the number of hidden layers of RBF neural network is 5, the precision of the model is high. Meanwhile, the constant inertia weight PSO-RBF neural network model is used as the comparison model, where \(w\) = 0.6, and the other parameters adopt the same value. The MSE and R2 calculations for the two models are shown in Table 7. The simulation results of the training set and test set are shown in Figure 8a,b, respectively. In comparison with the traditional PSO-RBF training model, it can be observed that the improved model is closer to the actual situation and the error is smaller. Therefore, it is feasible to use the IPSO-RBF model to evaluate pavement construction quality. Furthermore, when evaluating the construction quality of asphalt pavement for similar pavement, the corresponding evaluation index data can be substituted into the trained IPSO-RBF model and the corresponding PCQ value can be obtained during the construction process. Consequently, this approach can transform the post-quality inspection into in-process quality inspection, which allows the unqualified sections to be ascertained in a timely manner. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(n\) & 200 \\ \(k_{max}\) & 300 \\ \(w_{min}\) & 0.4 \\ \(w_{max}\) & 0.9 \\ \(c_{1}\) & 1.8 \\ \(c_{2}\) & 1.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Parameters of the PSO model. Figure 8: (**a**) Simulation results of the digital construction quality evaluation model of training set. (**b**) Simulation results of the digital construction quality evaluation model of test set. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Type** & **MSE** & **R2** \\ \hline PSO-RBF & 0.0796 & 0.9202 \\ IPSO-RBF & 0.0613 & 0.9475 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The comparative result between the two models. Correlation Analysis between the Comprehensive Evaluation Index of Pavement Construction Quality and Pavement Performance  
With the purpose of verifying the validity of the PCQ index proposed in this study, the pavement condition index (PCI) and international roughness index (IRI) of the above 30 sections after construction are investigated and collected. According to this approach the PCI index is detected by use of a comprehensive road inspection vehicle and the IRI index is measured by the continuous flatness meter method. The linear correlation analysis between PCQ and with both PCI and IRI is carried out by using IBM SPSS Statistics 26 software (International Business Machines Corporation, Armonk, NY, USA). The validity of the PCQ comprehensive evaluation index is verified by connecting multi-source data and the analysis results are shown in Figures 9 and 10. Figure 9 highlights that with the increase of PCI, PCQ gradually increases, thereby demonstrating a positive correlation.