The present paper focuses on the numerical modeling of a weigh-in-motion system developed with the purpose of assessing the static loads imposed by the train onto the track infrastructure. Weigh-in-motion systems would be useful in overcoming the disadvantages typical of the conventional static weighing such as costs and traffic management. However, contrary to the conventional static weighing, weigh-in-motion systems do not allow a direct measurement of the static load since the train–track dynamic interaction gives rise to dynamic loads that are added to the static ones. This study investigates how train speed and track unevenness affect the loads assessed by the weigh-in-motion system. In order to achieve that goal, a comprehensive statistical study was performed based on an extensive amount of calculations. Finally, based on the conclusions and trend identified through the comprehensive parametric study, an approach is proposed to correct the direct result given by the weigh-in-motion system in order to obtain an estimation of the static load.

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The gradual deterioration of train wheels can increase the risk of failure and lead to a higher rate of track deterioration, resulting in less reliable railway systems with higher maintenance costs. Early detection of potential wheel damages allows railway infrastructure managers to control railway operators, leading to lower infrastructure maintenance costs. This study focuses on identifying the type of sensors that can be adopted in a wayside monitoring system for wheel flat detection, as well as their optimal position. The study relies on a 3D numerical simulation of the train-track dynamic response to the presence of wheel flats. The shear and acceleration measurement points were defined in order to examine the sensitivity of the layout schemes not only to the type of sensors (strain gauge and accelerometer) but also to the position where they are installed. By considering the shear and accelerations evaluated in 19 positions of the track as inputs, the wheel flat was identified by the envelope spectrum approach using spectral kurtosis analysis. The influence of the type of sensors and their location on the accuracy of the wheel flat detection system is analyzed. Two types of trains were considered, namely the Alfa Pendular passenger vehicle and a freight wagon.

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