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Digital Twin Aided Sustainability and Vulnerability Audit for Subway Stations

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  • Sakdirat Kaewunruen

    (Department of Civil Engineering, School of Engineering, The University of Birmingham, Birmingham B15 2TT, UK)

  • Shijie Peng

    (Department of Civil Engineering, School of Engineering, The University of Birmingham, Birmingham B15 2TT, UK)

  • Olisa Phil-Ebosie

    (Transport for London (TfL), 5 Endeavour Square, London E20 1JN, UK)

Abstract

Digital twin (DT) or so-called ‘building information model (BIM)’ has brought great revolution to the current building industry. Its applications to life cycle management of buildings and infrastructures can further increase the social and economic benefits. As a complete information model, a digital twin integrates the information of a project from different stages of the life cycle into a model, in order to facilitate better asset management and communicate through data visualizations with participants. This paper unprecedently introduces a digital-twin aided life cycle assessment to evaluate a subway station. Dadongmen subway station in Hefei was used as a case study. This new study benchmarks the cost estimation and carbon emission at each life cycle stage of the project. The cost in the construction stage of the project is the highest, accounting for 78% of the total cost. However, the amount of carbon emissions in the operation and maintenance is higher than the amount during the production of building materials, accounting for 67%. Among them, concrete only accounts for 43.66% of the carbon emissions of building materials, even though concrete was mainly used for constructing the metro station. Steel bar and aluminum alloy have carbon emissions of 29.73% and 17.64%, respectively. In addition, emerging risks of the subway stations can be identified. The digital twin has been used to illustrate vulnerability and potential solutions to emerging risks, and to assess the suitability through life cycle cost and carbon footprint. This initiative is relatively new to the industry. The new insight into life cycle assessment or LCA (especially carbon footprint over the life cycle) integrated with digital twin applications will enable sustainable development that will enhance resilience of metro railway systems globally.

Suggested Citation

  • Sakdirat Kaewunruen & Shijie Peng & Olisa Phil-Ebosie, 2020. "Digital Twin Aided Sustainability and Vulnerability Audit for Subway Stations," Sustainability, MDPI, vol. 12(19), pages 1-17, September.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:19:p:7873-:d:418024
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    References listed on IDEAS

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    Cited by:

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    2. Joe F. Bozeman & Shauhrat S. Chopra & Philip James & Sajjad Muhammad & Hua Cai & Kangkang Tong & Maya Carrasquillo & Harold Rickenbacker & Destenie Nock & Weslynne Ashton & Oliver Heidrich & Sybil Der, 2023. "Three research priorities for just and sustainable urban systems: Now is the time to refocus," Journal of Industrial Ecology, Yale University, vol. 27(2), pages 382-394, April.
    3. João Vieira & João Poças Martins & Nuno Marques de Almeida & Hugo Patrício & João Gomes Morgado, 2022. "Towards Resilient and Sustainable Rail and Road Networks: A Systematic Literature Review on Digital Twins," Sustainability, MDPI, vol. 14(12), pages 1-23, June.
    4. Yongliang Deng & Ying Zhang & Zhenmin Yuan & Rita Yi Man Li & Tiantian Gu, 2023. "Analyzing Subway Operation Accidents Causations: Apriori Algorithm and Network Approaches," IJERPH, MDPI, vol. 20(4), pages 1-20, February.
    5. Lavinia Chiara Tagliabue & Fulvio Re Cecconi & Sebastiano Maltese & Stefano Rinaldi & Angelo Luigi Camillo Ciribini & Alessandra Flammini, 2021. "Leveraging Digital Twin for Sustainability Assessment of an Educational Building," Sustainability, MDPI, vol. 13(2), pages 1-16, January.
    6. Ágota Bányai & Tamás Bányai, 2022. "Real-Time Maintenance Policy Optimization in Manufacturing Systems: An Energy Efficiency and Emission-Based Approach," Sustainability, MDPI, vol. 14(17), pages 1-15, August.
    7. Sakdirat Kaewunruen & Jessada Sresakoolchai & Wentao Ma & Olisa Phil-Ebosie, 2021. "Digital Twin Aided Vulnerability Assessment and Risk-Based Maintenance Planning of Bridge Infrastructures Exposed to Extreme Conditions," Sustainability, MDPI, vol. 13(4), pages 1-18, February.

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