IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i13p7094-d581338.html
   My bibliography  Save this article

Heterogeneous Deterioration Process and Risk of Deficiencies of Aging Bridges for Transportation Asset Management

Author

Listed:
  • Daeseok Han

    (Sustainable Infrastructure Research Center, Korea Institute of Civil Engineering and Building Technology, Goyangdaero 283, Ilsanseo-gu, Goyang-si, Gyeonggi-do 10223, Korea)

Abstract

The government of the Republic of Korea has set the minimum service level of bridges as Grade B and has defined the risk management level as higher than 95 percent. To achieve this goal, it is necessary to understand the deterioration process and risk of deficiencies for bridges, and these characteristics should be reflected in the management strategy and budget investment plan. To this end, this study developed deterioration models according to the bridge ages to define heterogeneous deterioration characteristics of aging bridges. To build the deterioration models, this study collected and processed bridge diagnosis data for 10 years, and a Bayesian Markov mixed hazard model was introduced. Analysis results showed that the life expectancy of the aging bridges over 30 years was remarkably short, 1/3 of the average life expectancy of the network, and the probability of failure was seven times higher than that of new bridges within 10 years after completion. In addition, the optimal maintenance demand that satisfies a risk management level of 95 percent illustrated that 44.7 percent of the bridges at Grade C should be continuously maintained annually. The results showed that it is urgent to prepare a preemptive response strategy and budget-securing plan for aging bridges, which will rapidly increase to 39% in the next 10 years and 76% in 20 years.

Suggested Citation

  • Daeseok Han, 2021. "Heterogeneous Deterioration Process and Risk of Deficiencies of Aging Bridges for Transportation Asset Management," Sustainability, MDPI, vol. 13(13), pages 1-16, June.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:13:p:7094-:d:581338
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/13/7094/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/13/7094/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Koop,Gary & Poirier,Dale J. & Tobias,Justin L., 2007. "Bayesian Econometric Methods," Cambridge Books, Cambridge University Press, number 9780521671736, June.
    2. Chunfeng Wan & Zhenwei Zhou & Siyuan Li & Youliang Ding & Zhao Xu & Zegang Yang & Yefei Xia & Fangzhou Yin, 2019. "Development of a Bridge Management System Based on the Building Information Modeling Technology," Sustainability, MDPI, vol. 11(17), pages 1-17, August.
    3. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555.
    4. Chan,Joshua & Koop,Gary & Poirier,Dale J. & Tobias,Justin L., 2019. "Bayesian Econometric Methods," Cambridge Books, Cambridge University Press, number 9781108423380, September.
    5. Chan,Joshua & Koop,Gary & Poirier,Dale J. & Tobias,Justin L., 2019. "Bayesian Econometric Methods," Cambridge Books, Cambridge University Press, number 9781108437493, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Daeseok Han & Jin-Hyuk Lee & Ki-Tae Park, 2022. "Deterioration Models for Bridge Pavement Materials for a Life Cycle Cost Analysis," Sustainability, MDPI, vol. 14(18), pages 1-15, September.
    2. Asnake Adraro Angelo & Kotaro Sasai & Kiyoyuki Kaito, 2023. "Assessing Critical Road Sections: A Decision Matrix Approach Considering Safety and Pavement Condition," Sustainability, MDPI, vol. 15(9), pages 1-20, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Babatunde O. Abidoye & Joseph A. Herriges & Justin L. Tobias, 2012. "Controlling for Observed and Unobserved Site Characteristics in RUM Models of Recreation Demand," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 94(5), pages 1070-1093.
    2. Maksym, Obrizan, 2010. "A Bayesian Model of Sample Selection with a Discrete Outcome Variable," MPRA Paper 28577, University Library of Munich, Germany.
    3. Herriges, Joseph A. & Phaneuf, Daniel J. & Tobias, Justin L., 2008. "Estimating demand systems when outcomes are correlated counts," Journal of Econometrics, Elsevier, vol. 147(2), pages 282-298, December.
    4. Daeseok Han & Jin-Hyuk Lee & Ki-Tae Park, 2022. "Deterioration Models for Bridge Pavement Materials for a Life Cycle Cost Analysis," Sustainability, MDPI, vol. 14(18), pages 1-15, September.
    5. Christopher Moore & Daniel Phaneuf & Walter Thurman, 2011. "A Bayesian Bioeconometric Model of Invasive Species Control: The Case of the Hemlock Woolly Adelgid," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 50(1), pages 1-26, September.
    6. Peter Lenk, 2014. "Bayesian estimation of random utility models," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 20, pages 457-497, Edward Elgar Publishing.
    7. Park, Yuri & Koo, Yoonmo, 2016. "An empirical analysis of switching cost in the smartphone market in South Korea," Telecommunications Policy, Elsevier, vol. 40(4), pages 307-318.
    8. Maeng, Kyuho & Jeon, Seung Ryong & Park, Taeho & Cho, Youngsang, 2021. "Network effects of connected and autonomous vehicles in South Korea: A consumer preference approach," Research in Transportation Economics, Elsevier, vol. 90(C).
    9. Danaf, Mazen & Guevara, Angelo & Atasoy, Bilge & Ben-Akiva, Moshe, 2020. "Endogeneity in adaptive choice contexts: Choice-based recommender systems and adaptive stated preferences surveys," Journal of choice modelling, Elsevier, vol. 34(C).
    10. Danaf, Mazen & Guevara, C. Angelo & Ben-Akiva, Moshe, 2023. "A control-function correction for endogeneity in random coefficients models: The case of choice-based recommender systems," Journal of choice modelling, Elsevier, vol. 46(C).
    11. Abidoye, Babatunde Oluwakayode, 2010. "Bayesian inference in modeling recreation demand," ISU General Staff Papers 201001010800002496, Iowa State University, Department of Economics.
    12. Adnan Haider Bukhari & Safdar Ullah Khan, 2008. "A Small Open Economy DSGE Model for Pakistan," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 47(4), pages 963-1008.
    13. Richard S. J. Tol & In Chang Hwang & Frédéric Reynès, 2012. "The Effect of Learning on Climate Policy under Fat-tailed Uncertainty," Working Paper Series 5312, Department of Economics, University of Sussex Business School.
    14. Martinovici, A., 2019. "Revealing attention - how eye movements predict brand choice and moment of choice," Other publications TiSEM 7dca38a5-9f78-4aee-bd81-c, Tilburg University, School of Economics and Management.
    15. Wang, Hong & Forbes, Catherine S. & Fenech, Jean-Pierre & Vaz, John, 2020. "The determinants of bank loan recovery rates in good times and bad – New evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 177(C), pages 875-897.
    16. Francesco Furlanetto & Francesco Ravazzolo & Samad Sarferaz, 2019. "Identification of Financial Factors in Economic Fluctuations," The Economic Journal, Royal Economic Society, vol. 129(617), pages 311-337.
    17. Igari, Ryosuke & Hoshino, Takahiro, 2018. "A Bayesian data combination approach for repeated durations under unobserved missing indicators: Application to interpurchase-timing in marketing," Computational Statistics & Data Analysis, Elsevier, vol. 126(C), pages 150-166.
    18. Liu, De-Chih & Chang, Yu-Chien, 2022. "Systematic variations in exchange rate returns," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 569-583.
    19. Hasan, Iftekhar & Horvath, Roman & Mares, Jan, 2020. "Finance and wealth inequality," Journal of International Money and Finance, Elsevier, vol. 108(C).
    20. Obryan Poyser, 2017. "Exploring the determinants of Bitcoin's price: an application of Bayesian Structural Time Series," Papers 1706.01437, arXiv.org.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:13:p:7094-:d:581338. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.