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Quantifying the Third-Party Loss in Building Construction Sites Utilizing Claims Payouts: A Case Study in South Korea

Author

Listed:
  • Ji-Myong Kim

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

  • Kag-Cheon Ha

    (Department of Police & Fire Administration, U1 University, Yeongdong 29131, Korea)

  • Sungjin Ahn

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

  • Seunghyun Son

    (Department of Architectural Engineering, Kyung Hee University, Suwon 17104, Korea)

  • Kiyoung Son

    (School of Architectural Engineering, University of Ulsan, Ulsan 44610, Korea)

Abstract

This study aims to quantify the losses to third-parties on construction sites by determining the loss indicators and identifying the relationship between the losses and the indicators to improve the sustainability on building construction sites. The growing size and intricacy of recent construction projects have resulted in the growth of losses, both in quantity and frequency. Notably, third-party losses are rapidly increasing owing to the urbanization of the environment and increases in construction scale. Therefore, for efficient and sustainable construction management, a financial loss assessment model is essential to mitigate and manage such loss. This study uses the third-party losses on construction sites obtained from a major South Korean insurance company to describe the difference from the material losses and to disclose the loss indicators based on actual economic losses. ANOVA analysis and multiple regression analysis are adopted to identify the variance and define the loss indicators and to make prediction models, respectively. Several groups of loss indicators are investigated, including construction information and the occurrence of natural disasters. The findings and results of this research afford an essential guide to sustainable construction management, and they can serve as a first stage loss assessment model for construction projects.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:23:p:10153-:d:456987
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    References listed on IDEAS

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    1. Pengcheng Xiang & Fuyuan Jia & Xiaohui Li, 2018. "Critical Behavioral Risk Factors among Principal Participants in the Chinese Construction Industry," Sustainability, MDPI, vol. 10(9), pages 1-22, September.
    2. Sungjin Ahn & Taehui Kim & Ji-Myong Kim, 2020. "Sustainable Risk Assessment through the Analysis of Financial Losses from Third-Party Damage in Bridge Construction," Sustainability, MDPI, vol. 12(8), pages 1-15, April.
    3. 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.
    4. Chuk Kyo Kim, 2019. "Economic Development of Korea," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 11117, December.
    5. Ji-Myong Kim & Taehui Kim & Sungjin Ahn, 2020. "Loss Assessment for Sustainable Industrial Infrastructure: Focusing on Bridge Construction and Financial Losses," Sustainability, MDPI, vol. 12(13), pages 1-16, July.
    6. Henry Odeyinka, 2000. "An evaluation of the use of insurance in managing construction risks," Construction Management and Economics, Taylor & Francis Journals, vol. 18(5), pages 519-524.
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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. Ji-Myong Kim & Kwang-Kyun Lim & Sang-Guk Yum & Seunghyun Son, 2022. "A Deep Learning Model Development to Predict Safety Accidents for Sustainable Construction: A Case Study of Fall Accidents in South Korea," Sustainability, MDPI, vol. 14(3), pages 1-17, January.
    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. Ralf Dethlefsen & Luisa Orlik & Martin Müller & Aristomenis K. Exadaktylos & Stefan M. Scholz & Jolanta Klukowska-Rötzler & Mairi Ziaka, 2022. "Work-Related Injuries among Insured Construction Workers Presenting to a Swiss Adult Emergency Department: A Retrospective Study (2016–2020)," IJERPH, MDPI, vol. 19(18), pages 1-16, September.

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