1. Dynamic Quality Monitoring System to Assess the Quality of Asphalt Concrete Pavement
- Author
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Universitat Politècnica de València. Departamento de Proyectos de Ingeniería - Departament de Projectes d'Enginyeria, Chang'an University, Ministry of Education, China, Xi'an Science Technology Bureau, China Road and Bridge Corporation, Education Bureau of Shaanxi Province, National Social Science Fund of China, Natural Science Foundation of Qinghai, Ministry of Science and Technology, China, National Natural Science Foundation of China, Ma, Ziyuan, Zhang, Jingxiao, Philbin, Simon P., Li, Hui, Yang, Jie, Feng, Yunlong, Ballesteros-Pérez, Pablo, Skitmore, Martin, Universitat Politècnica de València. Departamento de Proyectos de Ingeniería - Departament de Projectes d'Enginyeria, Chang'an University, Ministry of Education, China, Xi'an Science Technology Bureau, China Road and Bridge Corporation, Education Bureau of Shaanxi Province, National Social Science Fund of China, Natural Science Foundation of Qinghai, Ministry of Science and Technology, China, National Natural Science Foundation of China, Ma, Ziyuan, Zhang, Jingxiao, Philbin, Simon P., Li, Hui, Yang, Jie, Feng, Yunlong, Ballesteros-Pérez, Pablo, and Skitmore, Martin
- Abstract
[EN] With the rapid development of new technologies, such as big data, the Internet of Things (IoT) and intelligent sensing, the traditional asphalt pavement construction quality evaluation method has been unable to meet the needs of road digital construction. At the same time, the development of such technologies enables a new management system for asphalt pavement construction. In this study, firstly, the dynamic quality monitoring system of asphalt concrete pavement is established by adopting the BeiDou Navigation Satellite System, intelligent sensing, the IoT and 5G technology. This allows key technical indicators to be collected and transmitted for the whole process of asphalt mixture, which includes the mixing plant, transport vehicle, paving and compaction. Secondly, combined with AHP and the entropy weight (EW) method, the index combination weight is calculated. The comprehensive index for the pavement digital construction quality index (PCQ) is proposed to reflect the impact of monitoring indicators on pavement quality. An expert decision-making model is formed by using the improved particle swarm optimization (PSO) algorithm coupled with radial basis function neural network (RBF). Finally, the digital monitoring index and pavement performance index are connected to establish a full-time and multi-dimensional digital construction quality evaluation model. This study is verified by a database created from the digital monitoring data of pavement construction collected from a highway construction project. The system proposed in this study can accurately reflect the quality of pavement digital construction and solve the lag problem existing in the feedback of construction site.
- Published
- 2021