One of the most common types of wheel damage is flats which can cause high maintenance costs and enhance the probability of failure and damage to the track components. This study aims to compare the performance of four feature extraction methods, namely, auto-regressive (AR), auto-regressive exogenous (ARX), principal component analysis (PCA), and continuous wavelet transform (CWT) capable of automatically distinguishing a defective wheel from a healthy one. The rail acceleration for the passage of freight vehicles is used as a reference measurement to perform this study which comprises four steps: (i) feature extraction from acquired responses using the specific feature extraction methods; (ii) feature normalization based on a latent variable method; (iii) data fusion to enhance the sensitivity to recognize defective wheels; and (iv) damage detection by performing an outlier analysis. The results of this research show that AR and ARX extraction methods are more efficient techniques than CWT and PCA for wheel flat damage detection. Furthermore, in almost every feature, a single sensor on the rail is sufficient to identify a defective wheel. Additionally, AR and ARX methods demonstrated the potential to distinguish a defective wheel on the left and right sides. Lastly, the ARX method demonstrated robustness to detect the wheel flat with accelerometers placed only in the sleepers.

The purpose of this research is to study the seismic performance of railway embankments through a probabilistic approach. Nonlinear response history analyses were conducted utilizing PLAXIS software. Three categories of railway embankments were selected and more than 2400 embankment-earthquake case studies were performed. Sensitivity analyses were implemented to obtain the most important variables in the seismic performance of railway embankments. Finally, analytical fragility curves were generated in terms of the mechanical properties of railway embankments (e.g., soil cohesion and friction angle). Fragility functions were developed, employing an incremental dynamic analysis approach using a set of ground motions, including near- and far-field earthquakes. The maximum vertical displacement of the embankment was chosen as a damage index parameter. Fragility curves were derived for three damage states, including slight, moderate and extensive damage, with respect to threshold values proposed in the literature. The results of this study revealed that the mechanical properties of embankments could be considered one of the crucial uncertainty factors in seismic fragility analysis of railway embankments.