An Integrated Software Suite for Rail Condition Analysis using Machine Learning

Rail transit and freight rail properties apply rail grinding to maintain rail condition and ensure satisfactory performance of rail infrastructure systems. The proposed research investigates and applies a variety of computationally intelligent algorithms to establish useful relationships between rail corrugation, noise generation, and vibration. These relationships will support more timely and effective rail grinding interventions. The algorithms will process real-world rail corrugation, noise, and vibration data collected from three rail transit properties in North America.

Applying Machine Learning to Develop Meaningful Rail Condition Indices

Rail transit and freight rail properties apply rail grinding to maintain rail condition and ensure satisfactory performance of rail infrastructure systems. The proposed research investigates and applies a variety of computationally intelligent algorithms to establish useful relationships between rail corrugation, noise generation, and vibration. These relationships will support more timely and effective rail grinding interventions. The algorithms will process real-world rail corrugation, noise, and vibration data collected from three rail transit properties in North America.