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Lifespan Prediction Technique for Digital Twin-Based Noise Barrier Tunnels

Author

Listed:
  • Jinwooung Kim

    (Department of Convergence Engineering for Future City, Sungkyunkwan University, Suwon 16419, Korea)

  • Sung-Ah Kim

    (Department of Architecture, Sungkyunkwan University, Suwon 16419, Korea)

Abstract

Noise barrier tunnels (NBTs) comprise prefabricated components that are easily replaceable, reusable, and have the capacity to improve performance when applied to components as their replacements. Reusing NBTs is more price competitive and environmentally friendly than producing new components. However, determining the remaining life of components before they need to be dismantled, using existing NBTs and their maintenance methods, is a considerable challenge. Therefore, this study used a prototype of the digital twin of an NBT to predict the condition and life of tunnel components using numerical behavior analyses. The prototype of the digital twin used in this study could be used to analyze behaviors using a gyro sensor, thereby enabling life analyses. Girders are structural components that have shorter life expectancies than components close to the ground. Component analysis results indicated that our proposed method confirmed changes in the life of girders and column components. Moreover, a negligible change in the life of the purlin component was confirmed. Life cycles management using digital twins can help reduce NBT installation costs, facilitate resource recycling, and make the installation process eco-friendly as they can aid in identifying components that require replacements in the initial designing stage and establish a procurement plan.

Suggested Citation

  • Jinwooung Kim & Sung-Ah Kim, 2020. "Lifespan Prediction Technique for Digital Twin-Based Noise Barrier Tunnels," Sustainability, MDPI, vol. 12(7), pages 1-14, April.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:7:p:2940-:d:342423
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    References listed on IDEAS

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    1. Fei Tao & Fangyuan Sui & Ang Liu & Qinglin Qi & Meng Zhang & Boyang Song & Zirong Guo & Stephen C.-Y. Lu & A. Y. C. Nee, 2019. "Digital twin-driven product design framework," International Journal of Production Research, Taylor & Francis Journals, vol. 57(12), pages 3935-3953, June.
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    Cited by:

    1. Özden Tozanlı & Elif Kongar & Surendra M. Gupta, 2020. "Evaluation of Waste Electronic Product Trade-in Strategies in Predictive Twin Disassembly Systems in the Era of Blockchain," Sustainability, MDPI, vol. 12(13), pages 1-33, July.
    2. Rong Xie & Muyan Chen & Weihuang Liu & Hongfei Jian & Yanjun Shi, 2021. "Digital Twin Technologies for Turbomachinery in a Life Cycle Perspective: A Review," Sustainability, MDPI, vol. 13(5), pages 1-22, February.
    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. Xiaoping Wu & Ye Zhu & Lingxiao Xian & Yingkai Huang, 2021. "Fatigue Life Prediction for Semi-Closed Noise Barrier of High-Speed Railway under Wind Load," Sustainability, MDPI, vol. 13(4), pages 1-18, February.

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