Abstract

Railway track engineers are focused on cost-effective track maintenance to support revenue train traffic. Therefore, effective detection of railway track maintenance needs and planning of maintenance activities are required to improve efficiency. One area requiring improvement to ensure safe train operations and efficient maintenance is identification and diagnosis of track support problems. This paper presents some of the research that was conducted to investigate the ability of ground penetrating radar (GPR) data to accurately measure railway track condition in an effort to help guide railway engineering and maintenance activities. The research effort included evaluation of data collection techniques, data interpretation methods, and comparison of GPR data to available track condition information. The main indicators of poor track condition detectable using GPR are increased moisture or changes in the ballast layer below the tie, both resulting in dielectric permittivity changes. Poor roadbed ballast condition generally is due to fouling, which indicates ballast contamination, typically due to particle abrasion, but potentially from other sources. Increased moisture is a strong indicator or poor track condition since trapped water can dramatically accelerate the ballast fouling process and fouled ballast tends to hold water in the track structure. The initial research indicated that GPR could be used to identify poor track conditions such as water trapped in the track structure and fouled ballast due to strong dielectric contrast between the tie and the supporting layers depending on ballast moisture and degree of fouling. Additional testing demonstrated the ability to identify obstacles to mechanized maintenance using GPR, although improvements are required to improve accuracy. Further research on the application of GPR to railway track engineering is required to further define the expected dielectric permittivity of common track materials and better target the transmitted energy from the GPR antenna to ensure the strongest signal from the track substructure layers is available for analysis.

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