Railways are facing significant challenges with the ever-growing demand for transportation, the imperative to reduce carbon emissions, and the need to maintain operational efficiency and safety. However, an increase in rail traffic and load can lead to an increase in degradation of the track components and thus reduce the infrastructure quality. Such degradation leads to failures of components, consequently resulting in a higher frequency of interventions for maintenance and renewal activities. Traditionally, railway track component inspection relies on visual based inspection techniques that are carried out during walking inspections or by using measurement cars. Manual inspections are slow, cost and labour intensive and are prone to human errors. Inspections based on measurement cars are cost intensive and requires track possession and thus cannot be utilised frequently without compromising the operational capacity. Further, the inclement weather conditions, especially in the north of Sweden, inhibits regular inspection based on these visual inspection methods. To overcome these challenges a train based differential eddy current sensor, based on the principle of electromagnetic induction, was developed to monitor railway track components. The sensor system was able to detect fastening system and identify missing clamps within the fastening system during regular operations. The aim of this study is to investigate the possibility of using the proposed sensor system to detect railway joints. Railway joints are where two rail segments meet, which makes them a structural weak point, and these are prone to wear, loosening, and misalignment over time. Constant monitoring helps to ensure that the joints maintain their integrity under varying weather conditions, heavy loads, and repeated stress. This paper will investigate the effectiveness of the proposed eddy current sensor to monitor such railway joints realised through laboratory and field measurements.

Ensuring rail infrastructure integrity is crucial for passenger safety and freight transport efficiency. Continuous monitoring of railway track components, therefore, plays a vital role in ensuring safe and reliable railway operations. Traditional inspection methods, such as manual and visual inspections, often miss subtle defects, especially when track components are obscured due to harsh environmental conditions, leading to increased risks and costly maintenance. To address these challenges, a differential eddy current sensor was developed that can be mounted on in-service trains for regular inspections. The current study aims to enhance the knowledge of the sensor capability by developing data driven and multi-physics simulation models to analyze the magnetic response of critical track components and their defects. Comprehensive simulations will consider interactions among the sensor, track components, operational and environmental conditions, and train dynamics, thus ensuring robust and resilient infrastructure. This paper proposes a methodology for developing a baseline multiphysics simulation model to generate synthetic data, enhancing and complementing data-driven approaches to improve the sensor system’s detection capabilities, addressing the need for an efficient inspection procedures and optimized maintenance practices. The findings highlight the potential of Multi-physics simulations in detecting critical rail defects and advancing railway infrastructure resilience.

Climate change and its severe impacts pose a number of challenges to transport infrastructure, particularly railway infrastructure, requiring immediate action. A railway system is a linear distributed asset passing different geographical locations and exposed to heterogeneous vulnerabilities under diverse environmental conditions. Furthermore, most of the railway infrastructure assets were designed and built without in-depth analysis of future climate impacts. This paper considers the effects of extreme temperatures on urban railway infrastructure assets, including rail, “switches and crossings”. The data for this study were gathered by exploring various railway infrastructure and meteorological databases over 19 years. In addition, a comprehensive nationwide questionnaire survey of Swedish railway infrastructure, railway maintenance companies, and municipalities has been conducted to assess the risks posed by climate change. A risk and vulnerability assessment framework for railway infrastructure assets is developed. The study shows that track buckling and vegetation fires due to the effect of hot temperatures and rail defects and breakage due to the effect of cold temperatures pose a medium risk. On the other hand, supportability losses due to cold temperatures are classified as high risk. The impact analysis helps infrastructure managers systematically identify and prioritize climate risks and develop appropriate climate adaptation measures and actions to cope with future climate change impacts.

Climate change impacts can escalate the deteriorating rate of infrastructures and impact the infrastructure’s functionality, safety, operation and maintenance (O&M). This research explores climate change’s influence on urban railway infrastructure. Given the geographical diversity of Sweden, the railway network is divided into different climate zones utilizing the K-means algorithm. Reliability analysis using the Cox Proportional Hazard Model is proposed to integrate meteorological parameters and operational factors to predict the degree of impacts of different climatic parameters on railway infrastructure assets. The proposed methodology is validated by selecting a number of switches and crossings (S&Cs), which are critical components in railways for changing the route, located in different urban railway stations across various climate zones in Sweden. The study explores various databases and proposes a climatic feature to identify climate-related risks of S&C assets. Furthermore, different meteorological covariates are analyzed to understand better the dependency between asset health and meteorological parameters. Infrastructure asset managers can tailor suitable climate adaptation measures based on geographical location, asset age, and other life cycle parameters by identifying vulnerable assets and determining significant covariates. Sensitivity analysis of significant covariates at one of the urban railway stations shows precipitation increment reveal considerable variation in the asset reliability.

Climate change impacts such as extreme temperatures, snow and ice, flooding, and sea level rise posed significant threats to railway infrastructure networks. One of the important questions that infrastructure managers need to answer is, “How will maintenance costs be affected due to climate change in different climate change scenarios?” This paper proposes an approach to estimate the implication of climate change on the life cycle cost (LCC) of railways infrastructure assets. The proportional hazard model is employed to capture the dynamic effects of climate change on reliability parameters and LCC of railway assets. A use-case from a railway in North Sweden is analyzed to validate the proposed process using data collected over 18 years. The results have shown that precipitation, temperature, and humidity are significant weather factors in selected use-case. Furthermore, our analyses show that LCC under future climate scenarios will be about 11 % higher than LCC without climate impacts.

Due to the accumulated greenhouse gas (GHG) effect, climate change will affect infrastructure networks regardless of different climate mitigation strategies. Our current investigation reveals an apparent increasing trend in the number of climatic-based failures in the Swedish railway infrastructure from 2010 until 2020.

Switch and crossing (S&C) is a critical part of the railway infrastructure network, which plays a key role in adjusting the railway network capacity and dependability performance. Due to the structure of S&C, it can be affected more by extreme climate change impacts, e.g., abnormal temperature, ice and snow, and flooding. Clearly, the reliability and hazard function of infrastructures will be affected by age and environmental conditions. Therefore, it is essential to analyze the effect of different climate change features / explanatory variables called "covariates" on the reliability of S&Cs. The proportional hazard model (PHM) is a practical approach to assess and prioritize the impact of various environmental covariates on S&Cs' reliability.

This paper aims to integrate climate change data with infrastructure asset health. This integration can be developed by utilizing proportional hazard methodology to assess the effect of different covariates on the reliability function. The proposed methodology has been verified through a number of S&Cs located on the Swedish railway network. As a main result, this study has revealed that the operational environment covariates significantly influence the reliability of S&Cs and profoundly affect the availability and capacity of railway tracks. The study indicates the need for effective climate adaptation options to reduce climate change impacts and risks to achieve resilience and climate-neutral railway infrastructure asset.

Wheelsets form an indispensable part of the railway rolling stock and need to be periodically inspected to ensure stable, safe, reliable, and sustainable rail operation. Wheel profiles are usually inspected and measured in a workshop environment using handheld equipment or by utilizing wayside measuring equipment. A common practice for both methods is to measure the wheel profile at one position along the circumference of the wheel, resulting in a one-slice measurement strategy, based on the assumption that the wheel profile has the same shape independent of the measurement position along the wheel. In this article, the representability of a one-slice measurement strategy with respect to the wheel profile parameters is investigated using handheld measurement equipment. The calculated range of standard deviation of the parameters estimated such as flange height, flange width, flange slope, and hollow wear from the measurements shows a spread in the parameter value along the circumference of the wheel. As an initial validation of the results, measurements from the wayside monitoring systems were also investigated to see if a similar spread was visible. The spread was significantly higher for flange height, flange width, and flange slope estimated from wayside measurement equipment than for the same parameters estimated using the handheld measurement equipment.