Overcoming strain gauges limitation in the estimation of train load passing on a bridge through deep learning

The estimation of trains weight could be useful under certain circumstances. For instance, in the field of structural health monitoring, some considerations can be derived from the evaluation of the load spectrum that an infrastructure has to withstand in its lifetime. One approach to estimate the train weight is based on the use of strain gauges mounted on the rail. The procedure allows to associate the local deformations with the load on an axle. However, strain gauges present several limitations: they are regarded as delicate sensors, and their replacement is burdensome and time-consuming. Moreover, their life is usually short when subjected to weathering and numerous load cycles. For these reasons, this paper proposes a novel methodology that relies on the use of more robust sensors mounted on a bridge structure for the estimation of the train load, alongside other information, such as the number of axles, the train speed, and the train class. The idea consists in the estimation of the train load starting from a network of sensors mounted on a bridge. A deep learning model is particularly suitable to achieve this task. The sensors network must consist of robust and easy-to-replace transducers (such as velocimeters mounted on the bridge structure). In this way, when the strain gauges are removed, the system is still able to estimate the loads passing on the bridge.