Effective Rail Neutral Temperature (RNT) management is needed for continuous welded rail (CWR). RNT is the temperature at which the longitudinal stress of a rail is zero. Due to the lack of expansion joints, CWR develops internal tensile or compressive stresses when the rail temperature is below or above, respectively, the RNT. Mismanagement of RNT can lead to rail fracture or buckling when thermal stresses exceed the limits of rail steel. In this work, we propose an effective RNT estimation method structured around four hypotheses. The work leverages field-collected vibration test data, high-fidelity numerical models, and machine learning techniques. First, a contactless non-destructive and non-disruptive sensing technology was developed to collect real-world rail vibrational data. Second, the team established an instrumented field test site at a revenue-service line in the state of Illinois and performed multi-day data collection to cover a wide range of temperature and thermal stress levels. Third, numerical models were developed to understand and predict rail vibration behavior under the influence of temperature and longitudinal load. Excellent agreement between model and experimental results were obtained using an optimization approach. Finally, a supervised machine learning algorithm was developed to estimate RNT using the field-collected rail vibration data. Sensitivity studies and error analyses were included in this work. The system performance with field data indicates that the proposed framework can support reasonable RNT estimation accuracy when measurement or model noise is low.

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