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Assessing the Risk of Natural Disaster-Induced Losses to Tunnel-Construction Projects Using Empirical Financial-Loss Data from South Korea

Author

Listed:
  • Sang-Guk Yum

    (Department of Civil Engineering and Engineering Mechanics, Columbia University, New York, NY 10027, USA)

  • Sungjin Ahn

    (Department of Architectural Engineering, Mokpo National University, Mokpo 58554, Korea)

  • Junseo Bae

    (School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley PA1 2BE, UK)

  • Ji-Myong Kim

    (Department of Architectural Engineering, Mokpo National University, Mokpo 58554, Korea)

Abstract

Tunnel construction, a common byproduct of rapid economic growth and transportation-system development, carries inherent risks to life and various kinds of property that operations and management professionals must take into account. Due to various and complicated geological conditions, tunnel construction projects can produce unexpected collapses, landslides, avalanches, and water-related hazards. Moreover, damage from such events can be intensified by other factors, including geological hazards caused by natural disasters, such as heavy rainfall and earthquakes, resulting in huge social, economic, and environmental losses. Therefore, the present research conducted multiple linear regression analyses on financial-loss data arising from tunnel construction in Korea to develop a novel tunnel-focused method of natural-hazard risk assessment. More specifically, the total insured value and actual value of damage to 277 tunnel-construction projects were utilized to identify significant natural-disaster indicators linked to unexpected construction-budget overruns and construction-scheduling delays. Damage ratios (i.e., actual losses over total insured project value) were used as objective, quantitative indices of the extent of damage that can be usefully applied irrespective of project size. Natural-hazard impact data—specifically wind speed, rainfall, and flood occurrences—were applied as the independent variables in the regression model. In the regression model, maximum wind speed was found to be correlated with tunnel projects’ financial losses across all three of the natural-hazard indicators. The present research results can serve as important baseline references for natural disaster-related risk assessments of tunnel-construction projects, and thus serve the wider purpose of balanced and sustainable development.

Suggested Citation

  • Sang-Guk Yum & Sungjin Ahn & Junseo Bae & Ji-Myong Kim, 2020. "Assessing the Risk of Natural Disaster-Induced Losses to Tunnel-Construction Projects Using Empirical Financial-Loss Data from South Korea," Sustainability, MDPI, vol. 12(19), pages 1-15, September.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:19:p:8026-:d:421103
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    References listed on IDEAS

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    Cited by:

    1. Junseo Bae & Sang-Guk Yum & Ji-Myong Kim, 2021. "Harnessing Machine Learning for Classifying Economic Damage Trends in Transportation Infrastructure Projects," Sustainability, MDPI, vol. 13(11), pages 1-12, June.
    2. Hao Pu & Jia Xie & Paul Schonfeld & Taoran Song & Wei Li & Jie Wang & Jianping Hu, 2021. "Railway Alignment Optimization in Mountainous Regions Considering Spatial Geological Hazards: A Sustainable Safety Perspective," Sustainability, MDPI, vol. 13(4), pages 1-22, February.
    3. Ji-Myong Kim & Junseo Bae & Seunghyun Son & Kiyoung Son & Sang-Guk Yum, 2021. "Development of Model to Predict Natural Disaster-Induced Financial Losses for Construction Projects Using Deep Learning Techniques," Sustainability, MDPI, vol. 13(9), pages 1-12, May.
    4. Ji-Myong Kim & Kag-Cheon Ha & Sungjin Ahn & Seunghyun Son & Kiyoung Son, 2020. "Quantifying the Third-Party Loss in Building Construction Sites Utilizing Claims Payouts: A Case Study in South Korea," Sustainability, MDPI, vol. 12(23), pages 1-13, December.
    5. Ping Liu & Yu Wang & Tongze Han & Jiaming Xu & Qiangnian Li, 2022. "Safety Evaluation of Subway Tunnel Construction under Extreme Rainfall Weather Conditions Based on Combination Weighting–Set Pair Analysis Model," Sustainability, MDPI, vol. 14(16), pages 1-21, August.

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