| 1. |
- Garmabaki, A.H.S., et al.
(författare)
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Assessing climate-induced risks to urban railway infrastructure
- 2024
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Ingår i: International Journal of Systems Assurance Engineering and Management. - : Springer. - 0975-6809 .- 0976-4348.
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 2. |
- Jägare, Veronica, et al.
(författare)
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System Innovation Challenges for Climate Adaptation
- 2024
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Ingår i: International Congress and Workshop on Industrial AI and eMaintenance 2023. - : Springer Science and Business Media Deutschland GmbH. ; , s. 707-721
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)
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| 3. |
- Kasraei, Ahmad, et al.
(författare)
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Climate Zone Reliability Analysis of Railway Assets
- 2024
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Ingår i: International Congress and Workshop on Industrial AI and eMaintenance 2023. - : Springer Science and Business Media Deutschland GmbH. ; , s. 221-235
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)
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| 4. |
- Kasraei, Ahmad, et al.
(författare)
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Reliability analysis of railway assets considering the impact of geographical and climatic properties
- 2024
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Ingår i: International Journal of Systems Assurance Engineering and Management. - : Springer Nature. - 0975-6809 .- 0976-4348.
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Tidskriftsartikel (refereegranskat)abstract
- Various factors, including climate change and geographical features, contribute to the deterioration of railway infrastructures over time. The impacts of climate change have caused significant damage to critical components, particularly switch and crossing (S&C) elements in the railway network. These components are sensitive to abnormal temperatures, snow and ice, and flooding, making them susceptible to failures. The consequences of S&C failures can have a detrimental effect on the reliability and safety of the entire railway network.It is crucial to have a reliable clustering of railway infrastructure assets based on various climate zones to make informed decisions for railway network operation and maintenance in the face of current and future climate scenarios. This study employs machine learning models to categorize S&Cs; therefore, historical maintenance data, asset registry information, inspection data, and weather data are leveraged to identify patterns and cluster failures. The analysis reveals four distinct clusters based on climatic patterns. The effectiveness of the proposed model is validated using S&C data from the Swedish railway network.By utilizing this clustering approach, the whole of Sweden railway network divided into 4 various groups. Utilizing this groups the development of model can associated with enhancing certainty of decision-making in railway operation and maintenance management. It provides a means to reduce uncertainty in model building, supporting robust and reliable decision-making. Additionally, this categorization supports infrastructure managers in implementing climate adaptation actions and maintenance activities management, ultimately contributing to developing a more resilient transport infrastructure.
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| 5. |
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| 6. |
- Soleimani-Chamkhorami, Khosro, et al.
(författare)
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Identifying climate-related failures in railway infrastructure using machine learning
- 2024
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Ingår i: Transportation Research Part D. - : Elsevier. - 1361-9209 .- 1879-2340. ; 135
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Tidskriftsartikel (refereegranskat)abstract
- Climate change impacts pose challenges to a dependable operation of railway infrastructure assets, thus necessitating understanding and mitigating its effects. This study proposes a machine learning framework to distinguish between climatic and non-climatic failures in railway infrastructure. The maintenance data of turnout assets from Sweden's railway were collected and integrated with asset design, geographical and meteorological parameters. Various machine learning algorithms were employed to classify failures across multiple time horizons. The Random Forest model demonstrated a high accuracy of 0.827 and stable F1-scores across all time horizons. The study identified minimum-temperature and quantity of snow and rain prior to the event as the most influential factors. The 24-hour time horizon prior to failure emerged as the most effective time window for the classification. The practical implications and applications include enhancement of maintenance and renewal process, supporting more effective resource allocation, and implementing climate adaptation measures towards resilience railway infrastructure management.
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