Track failures due to cross tie degradation or loss in ballast support may result in a number of problems ranging from simple service interruptions to derailments. Structural Health Monitoring (SHM) of railway track is important for safety reasons and to reduce downtime and maintenance costs. For this reason, novel and cost-effective track inspection technologies for assessing tracks’ health are currently insufficient and needed. Advancements achieved in recent years in cameras technology, optical sensors, and image-processing algorithms have made machine vision, Structure from Motion (SfM), and three-dimensional (3D) Digital Image Correlation (DIC) systems extremely appealing techniques for extracting structural deformations and geometry profiles. Therefore, optically based, non-contact measurement techniques may be used for assessing surface defects, rail and tie deflection profiles, and ballast condition. In this study, the design of two camera-based measurement systems is proposed for crossties-ballast condition assessment and track examination purposes. The first one consists of four pairs of cameras installed on the underside of a rail car to detect the induced deformation and displacement on the whole length of the track’s cross tie using 3D DIC measurement techniques. The second consists of another set of cameras using SfM techniques for obtaining a 3D rendering of the infrastructure from a series of two-dimensional (2D) images to evaluate the state of the track qualitatively. The feasibility of the proposed optical systems is evaluated through extensive laboratory tests, demonstrating their ability to measure parameters of interest (e.g. crosstie’s full-field displacement, vertical deflection, shape, etc.) for assessment and SHM of railroad track.
Alessandro Sabato, Christopher H. Beale, and Christopher Niezrecki, "A novel optical investigation technique for railroad track inspection and assessment," Proc. SPIE 10169, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, and Civil Infrastructure 2017, 101692C (Presented at SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring: March 29, 2017; Published: 19 April 2017); https://doi.org/10.1117/12.2257831.
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