A Method for Measuring the Mass of a Railroad Car Using an Artificial Neural Network

• Published in: Infrastructures (Basel) 2024-02, Vol.9 (2), p.31
• Main Authors: Denisenko, Mark A., Isaeva, Alina S., Sinyukin, Alexander S., Kovalev, Andrey V.
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial neural networks; Asphalt pavements; Bridges; Cars; Deformation; finite element modeling; Fourier transforms; load identification; Mass (Physics); Measurement; Measurement methods; Mechanical properties; multi-physics analysis; neural network; Neural networks; Position (location); Railroad cars; Railroad ties; Railroad wheels; Railroads; railway monitoring system; Sensors; Static loads; Strain gauges; Vehicles; Wavelet transforms; Weight
• ISSN: 2412-3811; 2412-3811
• DOI: 10.3390/infrastructures9020031

The fast, convenient, and accurate determination of railroad cars’ load mass is critical to ensure safety and allow asset counting in railway infrastructure. In this paper, we propose a method for modeling the mechanical deformations that occur in the rail web under the influence of a static load transmitted through a railway wheel. According to the proposed method, a railroad car’s weight can be determined from the rail deformation values. A solid model of a track section, including a railroad tie, rail, and wheel, is developed, and a multi-physics simulation technique that allows for the determination of the values of deformations and mechanical stresses in the strain gauge installation areas is presented. The influence of the loaded mass, the temperature of the rail, and the wheel position relative to the strain gauge location is considered. We also consider the possibility of using artificial neural networks to determine railroad cars’ weight without specifying the coordinates of the wheel position. The effect of noise in the data on the accuracy of determining the railroad car weight is considered.