Advertisement

KSCE Journal of Civil Engineering

, Volume 17, Issue 7, pp 1551–1561 | Cite as

Infrastructure asset management system for bridge projects in South Korea

  • Taehoon HongEmail author
  • Myung Jin Chae
  • Duyon Kim
  • Choongwan Koo
  • Kyo Sun Lee
  • Kyoung Ho Chin
Construction Management

Abstract

While there have been many studies on life cycle cost analysis and preventive maintenance planning, this study proposes an innovative method of bridge asset management in South Korea. Two different levels of approaches were used in this study. First, in the level of bridge elements, deterioration modeling and optimized maintenance repair and rehabilitation (MR&R) planning on bridge assets are proposed, using the bridge historical data of Han River in the city of Seoul. Second, the network level of bridge asset management is suggested, using historical MR&R cost and budget, overall-condition assessment results, and health index data. These two levels of approaches were developed into an Internet-based application so that facility managers can use them to review their past budgets and to plan their future budget based on historical data.

Keywords

bridge management system asset management markov chain life cycle costs deterioration modeling 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bulusu, S. and Sinha, K. C. (1997). “Comparison of methodologies to predict bridge deterioration.” Transportation Research Record: Journal of the Transportation Research Board, No. 1597, pp. 34–42.Google Scholar
  2. Butt, A. A., Shahin, M. Y., Feighan, K. J., and Carpenter, S. H. (1987). “Pavement performance prediction model using the Markov process.” Transportation Research Record, No. 1123, pp. 12–19.Google Scholar
  3. Cady, P. D. and Weyers, R. E. (1984). “Deterioration rates of concrete bridge decks.” Journal of Transportation Engineering, Vol. 110, No. 1, pp. 34–44.CrossRefGoogle Scholar
  4. Cesare, M. A., Santamarina, C., Turkstra, C., and Vanmarcke, E. H. (1992). “Modeling bridge deterioration with markov chains.” Journal of Transportation Engineering, Vol. 118, No. 6, pp. 820–833.CrossRefGoogle Scholar
  5. Chae, M. J. and Abraham, D. M. (2001). “Neuro-fuzzy approaches for sanitary sewer pipeline condition assessment.” Journal of Computing in Civil Engineering, Vol. 15, No. 1, pp. 4–14.CrossRefGoogle Scholar
  6. DeStefano, P. D. and Grivas, D. A. (1998). “Method for Estimating transition probability in bridge deterioration models.” Journal of Infrastructure Systems, Vol. 4, No. 2, pp. 56–62.CrossRefGoogle Scholar
  7. Durango-Cohen, P. L. (2004). “Maintenance and repair decision making for infrastructure facilities without a deterioration model.” Journal of Infrastructure Systems, Vol. 10, No. 1, pp. 1–8.CrossRefGoogle Scholar
  8. Hong, T. and Hastak, M. (2005). “MEMRRES: Model for evaluating maintenance, repair and rehabilitation strategies in concrete bridge decks.” Civil Engineering & Environmental Systems, Vol. 22, No. 4, pp. 233–248.CrossRefGoogle Scholar
  9. Hong, T. and Hastak, M. (2007). “Evaluation and determination of optimal MR&R strategies in concrete bridge decks.” Automation in Construction, Vol. 16, No. 2, pp. 165–175.CrossRefGoogle Scholar
  10. Hong, F. and Prozzi, J. A. (2006). “Estimation of pavement performance deterioration using Bayesian approach.” Journal of Infrastructure Systems, Vol. 12, No. 2, pp. 77–86.CrossRefGoogle Scholar
  11. Jiang, Y. and Sinha, K. C. (1989). “Bridge service life prediction model using the Markov chain.” Transportation Research Record, No. 1223, pp. 24–30.Google Scholar
  12. Morcous, G., Lounis, Z., and Mirza, M. S. (2003). “Identification of environmental categories for markovian deterioration models of bridge decks.” Journal of Bridge Engineering, Vol. 8, No. 6, pp. 353–361.CrossRefGoogle Scholar
  13. Morcous, G., Rivard, H., and Hanna, A. M. (2002). “Modeling bridge deterioration using case-based reasoning.” Journal of Infrastructure Systems, Vol. 8, No. 3, pp. 86–95.CrossRefGoogle Scholar
  14. Ortiz-Garcia, J. J., Costello, S. B., and Snaith, M. S. (2006). “Derivation of transition probability matrices for pavement deterioration modeling.” J. Transp. Eng., Vol. 132, No. 2, pp. 141–161.CrossRefGoogle Scholar
  15. Shepard, R. W. (2005). “Bridge management issues in a large agency, structure & infrastructure engineering: Maintenance, management.” Life-Cycle Design & Performance, Vol. 1, No. 2, pp. 159–164.Google Scholar
  16. Shepard, R. W. and Johnson, M. B. (2001). California bridge health index: A diagnostic tool to maximize bridge longevity investment, TR News, TRB, pp. 6–11.Google Scholar
  17. Thompson, P. D. and Johnson, M. B. (2005). “Markovian bridge deterioration: Developing models from historical data.” Structure & Infrastructure Engineering, Vol. 1, No. 1, pp. 85–91.CrossRefGoogle Scholar
  18. Veshosky, D., Beidleman, C. R., Buetow, G. W., and Demir, M. (1994). “Comparative analysis of bridge superstructure deterioration.” Journal of Structural Engineering, Vol. 120, No. 7, pp. 2123–2136.CrossRefGoogle Scholar
  19. Winston, W. L. (1994). Operations research: Applications and algorithms 3rd edition, Duxbury Press, ISBN 0534520200, Philadelphia.zbMATHGoogle Scholar

Copyright information

© Korean Society of Civil Engineers and Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Taehoon Hong
    • 1
    Email author
  • Myung Jin Chae
    • 2
  • Duyon Kim
    • 3
  • Choongwan Koo
    • 1
  • Kyo Sun Lee
    • 4
  • Kyoung Ho Chin
    • 5
  1. 1.Dept. of Architectural EngineeringYonsei UniversitySeoulKorea
  2. 2.Korea Institute of Construction TechnologyIlsanKorea
  3. 3.School of Construction EngineeringKyungil UniversityKyungsanKorea
  4. 4.Construction Engineering Management DivisionKorea Institute of Construction TechnologyIlsanKorea
  5. 5.Korea Institute of Construction TechnologyGoyangKorea

Personalised recommendations