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Publikace detail

Post-monitoring evolution: mathematical model for neutral temperature development in continuous welded rail
Rok: 2025
Druh publikace: článek v odborném periodiku
Název zdroje: International Journal of Rail Transportation
Název nakladatele: Taylor & Francis Ltd.
Místo vydání: Abingdon
Strana od-do: 1135-1156
Tituly:
Jazyk Název Abstrakt Klíčová slova
cze Post-monitoring evolution: mathematical model for neutral temperature development in continuous welded rail The long-term development of neutral temperature in continuous welded rail (CWR) was investigated using measurement systems equipped with self-compensated strain gauges in quarter bridge configuration. Field measurements on railway lines were conducted over a two-year period following the installation of the CWR. In tracks with lower traffic intensity and stiffness, the CWR neutral temperature could easily reach 40 degrees C during summer but dropped below 30 degrees C in winter. In contrast, in stiffer tracks with higher traffic intensity and heavier loads, the long-term deviation of the CWR neutral temperature remained within significantly narrower limits (closer to the CWR installation temperature - 23 degrees C). Regardless of track stiffness or traffic intensity, the trends in CWR neutral temperature development were found to be similar for the left and right rails within the same profile of monitored section. The proposed machine learning (ML)-enhanced, and regression-based mathematical models accurately represent the long-term development of neutral temperature in CWR. Thermal stress; strain gauge; continuous welded rail; railway; mathematical model; machine learning
eng Post-monitoring evolution: mathematical model for neutral temperature development in continuous welded rail The long-term development of neutral temperature in continuous welded rail (CWR) was investigated using measurement systems equipped with self-compensated strain gauges in quarter bridge configuration. Field measurements on railway lines were conducted over a two-year period following the installation of the CWR. In tracks with lower traffic intensity and stiffness, the CWR neutral temperature could easily reach 40 degrees C during summer but dropped below 30 degrees C in winter. In contrast, in stiffer tracks with higher traffic intensity and heavier loads, the long-term deviation of the CWR neutral temperature remained within significantly narrower limits (closer to the CWR installation temperature - 23 degrees C). Regardless of track stiffness or traffic intensity, the trends in CWR neutral temperature development were found to be similar for the left and right rails within the same profile of monitored section. The proposed machine learning (ML)-enhanced, and regression-based mathematical models accurately represent the long-term development of neutral temperature in CWR. Thermal stress; strain gauge; continuous welded rail; railway; mathematical model; machine learning

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