Toward Automated Field Ballast Condition Evaluation: Development of a Ballast Scanning Vehicle

Abstract

Ballast plays an essential role in the response of a railroad track to repeated loading. Ballast degradation may lead to poor drainage, lateral instability, and excessive settlement. Extreme levels of ballast degradation may cause service interruptions and safety concerns. Therefore, ballast condition evaluation is of great importance in ensuring safe and efficient operations. Current state-of-the-practice evaluation methods are heavily dependent on visual inspection, field sampling, and laboratory testing, which are subjective and labor-intensive. Meanwhile, existing inspection systems for railroads have not been customized for conducting in-depth evaluation of the ballast layer, including determination of the level of fouling and aggregate size and shape characteristics. For this reason, there is an urgent need for the development of a novel, vison-based ballast scanning platform. This paper introduces the ballast scanning vehicle (BSV), an automated platform that acquires high-quality images, videos, and 3-D height maps of ballast from plan and profile views of cut sections and trenches. The BSV is capable of performing data analysis and generating accurate and comprehensive evaluations of ballast conditions through geotechnical analyses. The essential design components, prototyping, and development stages are described. Further, preliminary data collected from testing on two in-service railroad tracks behind a shoulder ballast cleaner are presented to validate the functions of the BSV. The fully developed BSV serves as a data collection device for ballast evaluation and provides continuous and high-quality images for a deep learning-based computer vision algorithm for field ballast condition evaluation and geotechnical analyses.