Predicting Critical Speed of Railway Tracks Using Artificial Intelligence Algorithms

Abstract

Motivated by concerns regarding safety and maintenance, the operational speed of a railway line must remain significantly below the critical speed associated with the track–ground system. Given the large number of track sections within a railway corridor that potentially need to be analyzed, the development of efficient predictive tools is of the utmost importance. Based on that, the problem can be analyzed in a few seconds instead of taking several hours of computational effort, as required by a numerical analysis. In this context, and for the first time, machine learning algorithms, namely artificial neural networks and support vector machine techniques, are applied to this particular issue. For its derivation, a reliable and robust dataset was developed by means of advanced numerical methodologies that were previously experimentally validated. The database is available as supplemental data and may be used by other researchers. Regarding the prediction process, the performance of both models was very satisfactory. From the results achieved, it is possible to conclude that the prediction tool is a novel and reliable approach for an almost instantaneous prediction of critical speed in a high number of track sections.

Keywords

• artificial intelligence
• railway dynamics
• critical speed
• numerical modeling
• scoping tool