Rail Neutral Temperature Estimation Using Zero Group Velocity Modes and Machine Learning

With increasingly frequent extreme heat events over the past half century, thermal stress measurement and management of continuous welded rail (CWR) have become more important for railroad maintenance. Methods, including visual inspections and rail lifting, are routinely performed in railroad networks of the U.S. to prevent rail thermal buckling. When intervention becomes necessary, a rail distressing process, involving rail cutting and welding, will be performed to re-establish the zero-stress state at a desirable temperature. And the temperature at which the rail is stress-free is defined as rail neutral temperature (RNT). In this work, an RNT predictive tool that exploits zero group velocity (ZGV) modes and machine learning is proposed. First, the existence of ZGV modes in CWR is investigated through numerical simulation, using both semi-analytical finite element analysis (SAFE) and finite element (FE) models. Further, parametric studies are performed to quantify the effect of axial loads and rail temperature on ZGV modes. Additionally, the team established an instrumented field test site at a revenue-service line and performed multi-day data collection to cover a wide range of temperature and thermal stress levels. FE models were calibrated based on the field-collected vibrational data via a linear program optimization approach and an excellent agreement between model and experimental results was obtained. Finally, a supervised learning framework was developed to estimate the RNT using rail temperature and resonance frequencies as the inputs. The results show that the proposed framework can provide RNT estimation with reasonable accuracy (±5 ºF) when measurement noise is low.