This work focuses on the development and application of a methodology for detecting wheel polygonization in a Laagrss type rail freight vehicle based on dynamic responses induced by it on the track. To achieve that, a numerical train-track interaction model was adopted to simulate the passage of a train over a wayside monitoring system composed by a set of accelerometers installed on the rails. Based on an unsupervised method, a methodology for monitoring the condition of the railway vehicle wheels was applied in this work, using multivariate data analysis and processing techniques based on artificial intelligence. The extraction of features sensitive to the effect of wheel polygonization was performed using autoregressive (AR) and autoregressive models with exogenous inputs (ARX), principal component analysis (PCA) and wavelet transforms (CWT). Subsequently, data normalization techniques were used in relation to environmental and operational factors (based on PCA). Finally, data classification techniques capable of distinguishing states with and without damage based on Outlier and Cluster analysis were developed and applied to identify damage severity. The methodology proved to be effective in detecting the damage with very satisfactory results. Regarding the identification of the severity, some flaws are still verified.

Strong crosswinds may significantly affect the runnability of trains moving over viaducts. However, the actual influence of the viaduct’s behaviour under these circumstances in the performance of the train is still not rigorously discussed, particularly in high-pier viaducts characterized by large lateral flexibilities and very low frequencies. The present work aims to fill this gap, by studying the behaviour of the Arroyo de las Piedras high-pier viaduct, which belongs to the Spanish high-speed (HS) network, and the corresponding runnability of trains moving over it. To assess the actual influence of the lateral flexibility in the train’s performance, the study takes into consideration not only the original viaduct, but other scenarios with different flexibilities based on the original one. The vehicle’s performance is analysed in terms of running safety through different derailment criteria, as well as in terms of passenger comfort using the indexes stipulated in the norms. The results show that the influence of the lateral flexibility is almost negligible in the presence of crosswinds, since the main impact is related with the wind that hits the train directly, and not so much with the lateral movement of the deck.

The reliability of railway bridges is significantly influenced by so-called resonance states that appear in case the excitation frequency of the passing train is close to one of the natural frequencies of the railway bridge. In these resonant conditions, the dynamic response of the bridge is significantly increased, which can lead to higher maintenance costs, reduced passing speeds, and in the worst case, derailment of the train. In particular, the ballast of railway bridges significantly influences the response behaviour and should be take into account when evaluating the structural responses. In the proposed contribution, the influence of ballast behaviour on the structural responses in the state of resonance is of predominant interest. This includes the influence on the response peak at resonance and the resonance speed, i.e. the train speed at which the state of resonance appears. Three different railway bridge models are utilized in order to determine the influence of the ballast bed on the acceleration and displacement response of railway bridges subjected to high-speed trains. In the context of the finite element method, the ballast bed and track superstructure are implemented using linear or nonlinear dashpot and spring elements. The passing trains are modelled as moving single forces representing the static axle loads of a train. A reduced order modelling approach is utilized in order to significantly reduce the analysis time of the computationally demanding 2D finite element time history response analyses. Based on the evaluated so-called response spectra, consisting of absolute peak acceleration and displacement responses of the railway bridge for different train speeds, the resonant states are identified and the influence of nonlinear ballast behaviour is discussed and contrasted.