Evaluation of the evolution of the structure of cold recycled mixture subjected to wheel tracking using digital image processing

Journal of Electrical and Computer Engineering

Chen

2022

In the process of train operation, the contact friction between wheel and track will change the size of wheel set. In order to ensure the safe operation of train, it is of great significance to detect the change of wheel set size timely and accurately. On the basis of summarizing the research at home and abroad, the online dynamic detection technology of wheel set size is studied. Firstly, the overall architecture of the wheel set size online detection system is studied, the working principle of each detection module is explained, and the installation parameters of the laser displacement sensor are designed. Secondly, the machine learning algorithm of online dynamic detection technology of wheel set size is studied. The passing wheel sets are photographed by CCD camera, and then the three-dimensional model is reconstructed by point cloud data, and then the parameters of each part are measured and calculated according to the model. The wheel set size can be obtained.

Section: Detection Case Of Wheel Set Parametermentioning

confidence: 99%

Metro Wheel Set Size Measurement Method Based on CDD Image Recognition

Journal of Electrical and Computer Engineering

Chen

2022

“…As shown in Table 4, the research data for hot mix asphalt were given. Firstly, the regression coefficient of Table 3 was substituted into formula (2). Then, the PCI data obtained by the survey were fitted by 1stOpt.…”

Section: Equivalent Thickness Coefficient Calculationmentioning

confidence: 99%

“…These materials occupied a mass of land and polluted the environment. Generally, cold central plant recycling technology could recycle these old materials [1][2]. Although the technology has been widely used in China, the recycling rate of road pavement materials is less than 30%.…”

Section: Introductionmentioning

confidence: 99%

Equivalent Thickness Coefficient of Cold Central Plant Recycling Pavement Structure

Yang

Liang

et al. 2022

CEJ

Self Cite

This research focuses on the equivalent relationship between cold central plant recycling mixture and hot mix asphalt. In this paper, the pavement surface condition index (PCI) was calculated by the road surface conditions investigating, which was used as the evaluation standard. The unreasonable data were removed by SPSS software. The existing decay equation of pavement performance was simplified by MATLAB, and optimized by the Marquardt and global optimization methods. The survey data were fitted nonlinearly by 1stOpt software. Ultimately, the multivariate nonlinear regression optimization equation of the equivalent thickness coefficient was established. The results show that the cold central plant recycling with the emulsified asphalt (CREA) pavement equivalent thickness coefficient is 0.587. The cold central plant recycling with the foamed asphalt (CRFA) pavement equivalent thickness coefficient is 0.632. In addition, the performance of 10 cm cold central plant recycling pavement is equivalent to 6 cm the traditional hot mix asphalt pavement. The dispersion of the CREA is greater than that of the CRFA significantly.

“…Based on the BDS system and carrier phase splitting real-time kinematic (RTK) technique [34][35][36][37], coordinate information data transmission is carried out by installing reference stations and mobile stations on the construction site and construction machines and tools, respectively, so as to obtain the paving track, paving speed and paving time. In addition, multi-point infrared thermal imaging equipment is installed on the paver to acquire the overall pavement temperature of the asphalt mixture to monitor the paving temperature and pavement uniformity [38,39]. At the same time, the system display terminal LED screen enables operators to obtain visual paving information and thereby helps guide construction operations.…”

Section: Quality Control System For Asphalt Mixture Transportationmentioning

confidence: 99%

Dynamic Quality Monitoring System to Assess the Quality of Asphalt Concrete Pavement

Philbin

et al. 2021

Buildings

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.