Lifespan Prediction Technique for Digital Twin-Based Noise Barrier Tunnels
Jinwooung Kim and
Sung-Ah Kim
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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
Sustainability, 2020, vol. 12, issue 7, 1-14
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.
Keywords: noise barrier tunnel; construction; resource recycling; eco-friendly process (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (4)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:12:y:2020:i:7:p:2940-:d:342423
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