Profile evaluation of rail joint in a 3-m wavelength based on unsupervised learning

Cong，Jianli1; Yan，Xue2; Chen，Rong1; Gao，Mingyuan3; An，Boyang1; Tang，Huiyue4,5; Wang，Yuan6; Wang，Ping1

2022

10.1111/mice.12945

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING   影响因子和分区

1093-9687

1467-8667

Previously, electronic straightedges with a length of 1 m were widely used to measure the longitudinal profiles of rail joints. However, owing to the lack of an efficient measurement device, rail joints with 3-m wavelengths are seldom studied. In this study, a rail measurement trolley based on the chord-reference method was developed with a measurement wavelength of up to 3 m. A field measurement was performed on a 53-km metro line, and the waveforms of 4340 rail joints were obtained. First, to visualize the distribution of the dataset and to find out the common features, t-distributed stochastic neighbor embedding dimensionality reduction was applied to the rail joint dataset, and each rail joint waveform was mapped to a point in a two-dimensional space. Second, K-means was applied to the rail joint dataset, and six categories of rail joints were obtained. The results indicated that there are two types of rail joints: M-type and W-type, accounting for 18.41% and 76.08% of the total number of joints, respectively, and the remainder are bolted rail joints. Third, to better evaluate rail joint status, the concept of rail joint triangle (RJT) is proposed, and five shape-based features of a rail joint in 3-m wavelength are defined. Finally, using RJT distribution analysis, we observed that the shape-based features provide more essential information about a rail joint, such as symmetry, asymmetry, M-type, or W-type, compared with conventional indexes such as the quality index. Notably, compared with the waveform of a rail joint at 1 m, a 3-m waveform provides significantly more essential information, which can be meaningful for future research on the dynamic impact of rail joints, as well as profile grinding around rail joints. To help other researchers follow our research, our dataset is available on Mendeley Data (RWJ-3 m dataset).

SCI

英语

通讯

WOS:000888370600001

ENGINEERING

2-s2.0-85143429532

Scopus

1.School of Civil Engineering,Key Laboratory of High-speed Railway Engineering,Ministry of Education,Southwest Jiaotong University,Chengdu,Sichuan,China
2.North China Municipal Engineering Design & Research Institute Co. Ltd.,Tianjin,China
3.College of Engineering and Technology,Southwest University,Chongqing,China
4.National Supercomputing Center in Shenzhen,Shenzhen,Guangdong,China
5.Institute of Robotics and Intelligent Manufacturing and Shenzhen Institute of Artificial Intelligence and Robotics for Society,The Chinese University of Hong Kong,Shenzhen,Guangdong,China
6.School of System Design and Intelligent Manufacturing,Southern University of Science and Technology,Shenzhen,Guangdong,China

Cong，Jianli,Yan，Xue,Chen，Rong,et al. Profile evaluation of rail joint in a 3-m wavelength based on unsupervised learning[J]. COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING,2022.

Cong，Jianli.,Yan，Xue.,Chen，Rong.,Gao，Mingyuan.,An，Boyang.,...&Wang，Ping.(2022).Profile evaluation of rail joint in a 3-m wavelength based on unsupervised learning.COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING.

Cong，Jianli,et al."Profile evaluation of rail joint in a 3-m wavelength based on unsupervised learning".COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING (2022).