Adapting Railway Maintenance to Climate Change

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.

Detection and Classification System for Rail Surface Defects Based on Eddy Current

The prospect of growth of a railway system impacts both the network size and its occupation. Due to the overloaded infrastructure, it is necessary to increase reliability by adopting fast maintenance services to reach economic and security conditions. In this context, one major problem is the excessive friction caused by the wheels. This contingency may cause ruptures with severe consequences. While eddy’s current approaches are adequate to detect superficial damages in metal structures, there are still open challenges concerning automatic identification of rail defects. Herein, we propose an embedded system for online detection and location of rails defects based on eddy current. Moreover, we propose a new method to interpret eddy current signals by analyzing their wavelet transforms through a convolutional neural network. With this approach, the embedded system locates and classifies different types of anomalies, enabling an optimization of the railway maintenance plan. Field tests were performed, in which the rail anomalies were grouped in three classes: squids, weld and joints. The results showed a classification efficiency of ~98%, surpassing the most commonly used methods found in the literature.

Strength properties of ballast layer, created from new and recycled crushed stone ballast

The objective of the research is a railway ballast layer created from new and recycled ballast particles in different ratio. In order to study the possibility of using recycled ballast grains in ballast layer, it is necessary to carry out laboratory triaxial tests of ballast crushed stone with size of the particles 25-60 mm with different grain shape. Abrasion testing machine allows to reach the effect on new ballast, which is similar to abrasion of ballast particles in the railway track. Therefore, it is possible to create ballast samples from (new) mixed and recycled ballast and estimate which proportion has the strength characteristics, which are close to the ones in ballast layer created only with new ballast particles. The result of the study shows, that it makes sense to return the recycled crushed stone in a mix with a new one in order to reduce the cost on a railway maintenance.

Digital twins for managing railway maintenance and resilience

Background: To improve railway construction and maintenance, a novel digital twin that helps stakeholders visualize, share data, and monitor the progress and the condition during services is required. Building Information Modelling (BIM) is a digitalization tool, which adopts an interoperable concept that benefits the whole life-cycle assessment (LCA) of the project. BIM’s applications create higher performance on cost efficiency and optimal time schedule, helping to reduce any unexpected consumption and waste over the life cycle of the infrastructure. Methods: The digital twin will be developed using BIM embedded by the lifecycle analysis method. A case study based on Taipei Metro (TM) has been conducted to enhance the performance in operation and maintenance. Life cycles of TM will be assessed and complied with ISO14064. Operation and maintenance activities will be determined from official records provided by TM. Material flows, stocks, and potential risks in the LCA are analyzed using BIM quantification embedded by risk data layer obtained from TM. Greenhouse emission, cost consumption and expenditure will be considered for integration into the BIM. Results: BIM demonstrated strong potential to enable a digital twin for managing railway maintenance and resilience. Based on the case study, a key challenge for BIM in Taiwan is the lack of insights, essential data, and construction standards, and thus the practical adoption of BIM for railway maintenance and resilience management is still in the design phase. Conclusions: This study exhibits a practical paradigm of the digital twin for railway maintenance and resilience improvement. It will assist all stakeholders to engage in the design, construction, and maintenance enhancing the reduction in life cycle cost, energy consumption and carbon footprint. New insight based on the Taipei Mass Rapid Transit system is highly valuable for railway industry globally by increasing the lifecycle sustainability and improving resilience of railway systems.

Research on the Sustainability of the Standard Gauge Railway Construction Project in Tanzania

This exploratory study aimed to examine the validity of the sustainability evaluation model suggested and the sustainability of the standard gauge railway (SGR) construction project in Africa’s developing countries with Tanzania as a case study. By using the proposed railway sustainability evaluation model, the researchers collected data from 300 people, which included workers in the project and people living or conducting businesses along the route of the first phase of the project (Dar-es-salaam to Morogoro). Data was collected using semi-structured questionnaires and analyzed using the structural equation model (SEM) technique and correlation analysis. The findings validated the model used to be efficient in railway sustainability evaluation, and also, the researchers were able to realize from the data collected that the first phase of the SGR in Tanzania is sustainable for development. The study suggests encouraging and collaborating with local agencies to improve local railway maintenance and operations skills in order to ensure the project’s long-term viability and extension.

Enhancing Railway Maintenance Safety Using Open-Source Computer Vision

As high-speed railways continue to be constructed, more maintenance work is needed to ensure smooth operation. However, this leads to frequent accidents involving maintenance workers at the tracks. Although the number of such accidents is decreasing, there is an increase in the number of casualties. When a maintenance worker is hit by a train, it invariably results in a fatality; this is a serious social issue. To address this problem, this study utilized the tunnel monitoring system installed on trains to prevent railway accidents. This was achieved by using a system that uses image data from the tunnel monitoring system to recognize railway signs and railway tracks and detect maintenance workers on the tracks. Images of railway signs, tracks, and maintenance workers on the tracks were recorded through image data. The Computer Vision OpenCV library was utilized to extract the image data. A recognition and detection algorithm for railway signs, tracks, and maintenance workers was constructed to improve the accuracy of the developed prevention system.