Article Article
A Machine Learning Framework for Rail Neutral Temperature Estimation using Impulse Vibrational Responses

Longitudinal rail force management of continuous welded rail (CWR) is important for safe and efficient railroad operation. A key parameter to measure and monitor is the rail neutral temperature (RNT) or the stress-free temperature. The team proposed a supervised learning framework to estimate the RNT using impulse vibrational responses from CWRs. We first established an instrumented field site on a revenue-service line and collected impulse vibrational response data covering a wide range of temperature and thermal stress. Then, we trained a data-driven model that uses rail temperatures and modal frequencies as the input for in-situ RNT prediction. The results demonstrated that the proposed framework could provide RNT estimation with a reasonable precision (±5 ºF)

DOI: 10.32548/RS.2022.021

References

(1) Association of American Railroads, “Overview of America’s Freight Railroads,” March 2020.

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(3) Knopf, K., Rizos, D. C., Qian, Y., and Sutton, M., 2020, “A non-contacting system for rail neutral temperature and stress measurements: Concept development,” Structural Health Monitoring, 20(1), pp 84-100.

(4) Zhu, X. and Lanza di Scalea, F., 2017, “Thermal Stress Measurement in Continuous Welded Rails Using the Hole-Drilling Method,” Experimental Mechanics, 57, pp 165-178.

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