Broken rail is the most common type of mainline derailment cause on freight railroads in the United States. Detection and removal of rail defects is important for reducing the risk due to broken-rail-caused derailments. The current practice is to periodically inspect rails using non-destructive technologies, particularly ultrasonic inspection. Determining and prioritizing the frequency of rail defect inspection is an important decision in broken rail risk management. A generalized, risk-based mixed integer nonlinear programming (MINLP) model is developed which can optimize segment-specific rail defect inspection frequency to minimize route broken rail risk, especially under limited inspection resources. A numerical example to optimize the inspection frequency is used to illustrate the application of the model. The result analysis states that the optimization approach can lead to a risk reduction of broken rail compared to an empirical heuristic that all segments on the same route are tested at an equal frequency. A computer-aided decision making tool called “Rail Risk Optimizer” can be developed and implemented based on this risk-based optimization algorithm that automatically recommend an optimal segment-specific inspection scheduling. The tool will consider the risk factors such as rail age and annual traffic density to determine the segment-specific risk level. The research methodology and the practice-ready optimization tool can aid the railroad industry to mitigate broken rail risk in a cost-efficient manner.

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