Maintenance time identification and deterioration rate analysis for ballastless track sections

HE Qing; DENG Yajie; SUN Huakun; LI Kun; XU Yingli; WANG Ping

doi:10.11860/j.issn.1673-0291.20230138

Abstract：In response to the issue of historical maintenance record deficits in high-speed railway linesdue to insufficient informatization in the early stages of operation, thereby impacting the rationalplanning of high-speed railway operation and maintenance in later stages, a model for identifyingmaintenance times of high and low track irregularities using information entropy and numerical filteringis established. Firstly, based on mining long-term track dynamic inspection data, precise maintenanceinformation is obtained, revealing the relationship between track maintenance and irregularitydeterioration rate. Next, the model's extracted time nodes are compared with historical maintenancedata of CRTS Ⅰ slab track for assessment of extraction effectiveness. Finally, linear regression is usedto predict and analyze deterioration rates during different deterioration periods. Results indicate varying degrees of improvement among maintenance schemes: 18％ of schemes achieve a 70％ improvement in the original high and low irregularity data, while 75％ achieve a 30％ improvement. Comparison across different substructure types reveals that the longest average maintenance cycle for the top 10％ deteriorating track sections is found in tunnel sections at 28 months, and the shortest in bridge sections at 9 months. Sections without maintenance account for 18％ of the total track, with the standard devia-tion of high and low irregularity deterioration rates being relatively concentrated below 0.01 mm/month. Since January 2016, relying on routine inspection and maintenance activities, the earliest estimated next maintenance time is expected in 82 months.