The rail fastening system plays a crucial role in railway tracks as it ensures operational safety by fixing the rail on to the sleeper. Early detection of rail fastener system defects is crucial to ensure track safety and to enable maintenance optimization. Fastener inspections are normally conducted either manually by trained maintenance personnel or by using automated 2-D visual inspection methods. Such methods have drawbacks when visibility is limited, and they are also found to be expensive in terms of system maintenance cost and track possession time. In a previous study, the authors proposed a train-based differential eddy current sensor system based on the principle of electromagnetic induction for fastener inspection that could overcome the challenges mentioned above. The detection in the previous study was carried out with the aid of a supervised machine learning algorithm. This study reports the finding of a case study, along a heavy haul line in the north of Sweden, using the same eddy current sensor system mounted on an in-service freight train. In this study, unsupervised machine learning models for detecting and analyzing missing clamps in a fastener system were developed. The differential eddy current measurement system was set to use a driving field frequency of 27 kHz. An anomaly detection model combining isolation forest (IF) and connectivity-based outlier factor (COF) was implemented to detect anomalies from fastener inspection measurements. To group the anomalies into meaningful clusters and to detect missing clamps within the fastening system, an unsupervised clustering based on the DBSCAN algorithm was also implemented. The models were verified by measuring a section of the track for which the track conditions were known. The proposed anomaly detection model had a detection accuracy of 96.79% and also exhibited a high score of sensitivity and specificity. The DBSCAN model was successful in clustering missing clamps, both one and two missing clamps, from a fastening system separately. Full article

Railway infrastructure is vulnerable to extreme weather events such as elevated temperature, flooding, storms, intense winds, sea level rise, poor visibility, etc. These events have extreme consequences for the dependability of railway infrastructure and the acceptable level of services by infrastructure managers and other stakeholders. It is quite complex and difficult to quantify the consequences of climate change on railway infrastructure because of the inherent nature of the railway itself. Hence, the main aim of this work is to qualitatively identify and assess the impact of climate change on railway infrastructure with associated risks and consequences. A qualitative research methodology is employed in the study using a questionnaire as a tool for information gathering from experts from several municipalities in Sweden, Swedish transport infrastructure managers, maintenance organizations, and train operators. The outcome of this questionnaire revealed that there was a lower level of awareness about the impact of climate change on the various facets of railway infrastructure. Furthermore, the work identifies the challenges and barriers for climate adaptation of railway infrastructure and suggests recommended actions to improve the resilience towards climate change. It also provides recommendations, including adaptation options to ensure an effective and efficient railway transport service. Full article

Ballasted tracks are common in the railway system as a means of providing the necessary support for the sleepers and the rails. To keep them operational, tamping and other maintenance actions are performed based on track geometry measurements. Ballast particle rearrangement, which is caused by train load, is one of the most important factors leading to track degradation. As a result, when planning maintenance, it is vital to predict the behaviour of the ballast under cyclic loading. Since ballast is a granular matter with a nonlinear and discontinuous mechanical behaviour, the discrete element method (DEM) was used in this paper to model the ballast particle rearrangement under cyclic loading. We studied the performance of linear and nonlinear models in simulating the settlement of the sleeper, the lateral deformation of the ballast shoulder and the porosity changes under the sleeper. The models were evaluated based on their ability to mimic the ballast degradation pattern in vertical and lateral direction. The linear contact model and the hysteretic contact model were used in the simulations, and the effect of the friction coefficient and different damping models on the simulations was assessed. An outcome of this study was that a nonlinear model was proposed in which both the linear and the hysteretic contact models are combined. The simulation of the sleeper settlement and the changes in the porosity under the sleeper improved in the proposed nonlinear model, while the computation time required for the proposed model decreased compared to that required for the linear model. Full article

The rail fastening system forms an indispensable part of the rail tracks and needs to be periodically inspected to ensure safe, reliable and sustainable rail operations. Automated visual inspection has gained significant importance for fastener inspection in recent years. Position accuracy, robustness, and practical limitations due to the complex environment are some of the major concerns associated with this method. This study investigates the combined use of image processing and deep learning algorithms for detecting missing clamps within a rail fastening system. The images used for this study was acquired during field inspections carried out along the Borlänge-Avesta line in Sweden. The image processing techniques proposed in this study enabled the improvement of the fastener position and removal of redundant information from the fastener images. In addition, image augmentation was carried out to enhance the data set, ensure experimental reliability and replicate practical challenges associated with such visual inspection. Convolutional neural network and ResNet-50 algorithms are used for classification purposes, and both the algorithms achieved over 98% accuracy during training and validation and over 94% accuracy during the test stage. Both the algorithms also maintained a good balance between the precision and recall scores during the test stage. CNN and ResNet-50 algorithms were also tested to analyse their performances when the clamp areas were covered. CNN was able to accurately predict the fastener state up to 70% of clamp area occlusion, and ResNet-50 was able to achieve accurate predictions up to 75% of clamp area occlusion. Full article