Fractal-Based Modeling Of Rail Roughness

This paper uses fractal modeling techniques to effectively depict the roughness characteristics of the rails examined in this study by means of both the structure function and the Weierstrass–Mandelbrot function to capture the intricate nature of the rail roughness. Experimental analysis of the roughness of the railway rails from Faurei Railway Testing Center (RTC Faurei) in Romania proves that the roughness height exhibits distinct mathematical fractal characteristics. The study evaluated and compared 41 classical statistical parameters derived from roughness measurements with simulated fractal parameters. The classical roughness parameters, including the Autocorrelation function, Amplitude Density Function, as well as Bearing Area Curves, and rail acoustic roughness, were determined from a profile obtained using the Weierstrass function and compared to the measured ones, revealing a noticeable congruence between the generated charts. The research findings indicate a strong convergence between experimental measurements and simulated profiles, with most parameters falling within a 10% relative error range. This aspect highlights the approach of fractal potent in assessing rail roughness behavior. Consequently, the simulated parameters could potentially analyze rail roughness quality to maintain and track grinding and mitigate the rolling noise.