1. Introduction
The construction of road transportation infrastructure continues to be a major sector across the world and pavement construction quality management has become an increasingly important issue to be addressed [
1,
2,
3]. However, at present traditional asphalt pavement detection approach often adopts the manual recording method, which has obvious disadvantages, such as the potential for mistakes to be made and the difficulty in transferring the information digitally. In view of the lack of effective real-time supervision of certain key processes in the construction industry in conjunction with poor levels of digital technology adoption, it is difficult to detect and rectify quality problems in a timely fashion, which leads to the shortened service life of asphalt pavements [
4,
5,
6]. Therefore, empirical research on digital construction quality management of asphalt pavement is of major significance in regard to the quality process control for pavement construction [
7,
8,
9].
At present, many industrial sectors in China are accelerating the development of information technology as part of the process of digital transformation and this involves both digitization of data from manual recording to digital systems as well as digitalization of existing business processes into digital systems. For example, Sun [
10] and others have applied the building information modeling (BIM) system to the quality management of construction engineering for the purpose of quality evaluation. Furthermore, Zhong [
11] and others have applied Internet of Things (IoT) technology to the analysis of bridge construction management for information collection. The application of these information technologies has laid a foundation for the development of digital monitoring technology for asphalt pavement construction in highway engineering construction.
Adoption of digital monitoring technology for asphalt pavement construction can enable the realization of remote control tracking of the construction process. This has the potential to transform the previous manual inspection process into a digital process control system in order to enhance the efficient and effective quality control of highway construction projects. Indeed, Zhang [
12] and others have developed an intelligent sensing aggregate (ISA) using advanced technologies, such as 3D printing and the Internet of Things. The system can collect and analyze data in real time, and the equipment also has resistance to high temperature. Hu [
13] studied the use of an intelligent compactor to detect the compaction meter value (CMV) of pavement, and thereafter explored the relationship between CMV and asphalt pavement density. Moreover, Fares [
14] used the intelligent asphalt compaction analyzer (IACA) to estimate the density during asphalt pavement construction so as to determine the quality of pavement compaction. It can therefore be observed that in existing research scholars have mainly focused on the use of digital monitoring equipment for the construction process of asphalt pavement. However, there appears to be a lack of research on the application of quality control tools for the whole process of pavement construction.
In order to make a more effective judgement on the construction quality of asphalt pavement, it is necessary to evaluate the different quality management systems. Zhang [
15] proposed a pavement construction quality index system based on Internet of Things technology, which determined the weight of each index by AHP (analytic hierarchy process) and divided the comprehensive evaluation grade, whereas Hosseini [
16] and others defined production and construction quality control indicators, and analyzed the relationship between pavement construction quality control indicators and pavement disease indicators. Zhou [
17] established the data analysis system for asphalt pavement engineering quality process control by using the method of consistency checking, which realized the dynamic control of the asphalt pavement engineering quality process. Further, Zhang [
18] introduced the percentage within limits method to quantify the quality of each index, and established the construction quality evaluation model for different layers of asphalt pavement by using AHP. While existing evaluation methods reflect the reliability and satisfaction of pavement construction quality, the key indicators for the pavement construction process monitored by information technology have not been fully considered. Consequently, an effective system that properly reflects the relationship between construction quality indicators and pavement performance still needs to be investigated and developed. Such an approach will improve the work efficiency of the constructor as well as for the owner and the supervisor, and thereby reduce the construction management costs.
The research study reported in this article aims to realize effective management of the whole process of asphalt pavement construction. In order to achieve this goal, the study adopted a range of technologies, namely the BeiDou satellite (BDS) navigation system combined with Internet of Things and intelligent sensing technology in order to develop the digital monitoring system for measuring asphalt pavement quality. The technology solution also uses a 5G wireless network to collect and transmit the key parameters collected of the main equipment in the asphalt pavement construction process. Through real-time and dynamic secondary mining of the collected parameters the data analysis and evaluation module was established. Based on this approach, this empirical study proposes the pavement digital construction quality index PCQ, and establishes the relationship model between the monitoring index and PCQ based on an IPSO-RBF neural network. The study verifies the effectiveness of the quality evaluation index through analyzing the quantitative correlation between PCQ and both the Pavement Condition Indicator (PCI) and International Roughness Index (IRI). This methodology offers a new way to enable quantitative evaluation of asphalt pavement quality.
5. Example Verification and Model Training
5.1. 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.
5.2. Model Training and Simulation Results
On the basis of the actual construction situation of the highway project, a continuous 1 km section is defined as an evaluation section. A total of 30 sections are selected from the database as data samples, including 20 sets of data as training samples and 10 sets of data as test samples. The PWL score is calculated by querying the PWL table according to the actual data value of each index. The samples are trained by MATLAB 2016a software (The MathWorks, Inc., Natick, MA, USA). The PWL values of 12 evaluation indexes are 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.
5.3. 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
Figure 9 and
Figure 10.
Figure 9 highlights that with the increase of PCI, PCQ gradually increases, thereby demonstrating a positive correlation. From
Figure 10 it can be observed that as IRI increases, PCQ gradually decreases, thereby demonstrating a negative correlation. Indeed, PCI has a strong correlation with PCQ and IRI, which is consistent with the actual construction of pavement. Therefore, PCQ represents an effective method to evaluate the construction quality of asphalt pavement.
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 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.