# Dynamic Quality Monitoring System to Assess the Quality of Asphal Concrete Payement
## 6 Discussion  
In order to enable the digital transformation and improved quality management of pavement construction, this study has developed a dynamic quality monitoring system for  
Figure 10: Correlation analysis between PCQ and IRI. Figure 9: Correlation analysis between PCQ and PCI. asphalt concrete pavement based on the BDS system in conjunction with other new digital technologies. The system implementation covers the whole process of asphalt mixture production, transportation, on-site paving and rolling, which provides a new method for asphalt pavement digital construction. Internet of Things, 5G and GNSS technologies have been applied to all key aspects of asphalt pavement construction. This approach realizes the digital management of pavement construction combined with the timeliness and traceability of quality evaluation data. Compared with the traditional monitoring method, the new system solves the problem of lag of monitoring data of pavement construction, so that measures can be identified and pursued to solve the possible pavement construction quality problems in a timely fashion. At the same time and through advancing our understanding of research on digital construction quality evaluation of asphalt pavement, this study further discusses the key influencing factors of digital construction quality evaluation as the object of digital acquisition, transmission and analysis of the asphalt pavement construction process, and establishes a complete digital monitoring system for pavement construction. Moreover, a decision model of asphalt pavement quality evaluation based on the IPSO-RBF neural network is established to further investigate the relationship between monitoring parameters and pavement performance, and a new quantitative index PCQ is proposed. In this study, 30 road sections are selected for road construction quality evaluation. The illustrative example shows that PCQ can accurately reflect the pavement quality, and the IPSO-RBF neural network model can be deployed to evaluate pavement quality quickly and precisely, which underscores the application of this research study in the asphalt pavement construction sector. In this empirical study the relationship between performance indexes after completion of pavement construction with the comprehensive evaluation index PCQ of pavement construction (involving asphalt mixture production, transportation, paving and rolling) has only been explored preliminarily and consequently the process requires further development. Therefore, it is essential to further study the relevance between PCQ and the performance indicators of pavement use and the maintenance stage to calculate and determine the controlling index range of PCQ. Such an approach will need to establish the evaluation criteria for PCQ indicators with probabilistic curves created by the data collected so that the model achieves more robust generalization. Conceptualization, Z.M. and J.Z. ; Methodology, Z.M. ; Software, J.Z. and H.L. ; Validation, S.P.P. and J.Z. ; Formal Analysis, P.B.-P. and M.S. ; Investigation, J.Y. and Y.F. ; Resources, Z.M., P.B.-P. and M.S. ; Data Curation, J.Y. and Y.F. ; Writing--Original Draft Preparation, Z.M. and H.L. ; Writing--Review & Editing, Z.M., J.Z and S.P.P. ; Visualization, S.P.P. ; Supervision, J.Z. ; Project Administration, J.Z. All authors have read and agreed to the published version of the manuscript.