IDEAS home Printed from https://ideas.repec.org/p/cdl/uctcwp/qt5k01s7x9.html
   My bibliography  Save this paper

Dynamic Programing based Maintenance and Replacement Optimization for Bridge Decks using History-Dependent Deterioration Models

Author

Listed:
  • Robelin, Charles-Antoine
  • Madanat, S M

Abstract

In this research, a reliability-based optimization model of bridge maintenance and replacement decisions is developed. Bridge maintenance optimization models use deterioration models to predict the future condition of bridges. Some current optimization models use physically-based models taking into account the history of deterioration. However, due to the complexity of the deterioration models, the number of decision variables in these optimization models is limited. Some other optimization models consist of a full set of decision variables; however, they use simpler deterioration models. Namely, these deterioration models are Markovian, and the state of the Markov chain is limited to the condition of the facility.

Suggested Citation

  • Robelin, Charles-Antoine & Madanat, S M, 2006. "Dynamic Programing based Maintenance and Replacement Optimization for Bridge Decks using History-Dependent Deterioration Models," University of California Transportation Center, Working Papers qt5k01s7x9, University of California Transportation Center.
    • Handle: RePEc:cdl:uctcwp:qt5k01s7x9
    as

    Download full text from publisher

    File URL: https://www.escholarship.org/uc/item/5k01s7x9.pdf;origin=repeccitec
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Qi-Neng Zhou & Ye Yuan & Dong Yang & Jing Zhang, 2022. "An Advanced Multi-Agent Reinforcement Learning Framework of Bridge Maintenance Policy Formulation," Sustainability, MDPI, vol. 14(16), pages 1-18, August.
    2. Pinciroli, Luca & Baraldi, Piero & Zio, Enrico, 2023. "Maintenance optimization in industry 4.0," Reliability Engineering and System Safety, Elsevier, vol. 234(C).

    More about this item

    Keywords

    Social and Behavioral Sciences;

    Statistics

    Access and download statistics

    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:cdl:uctcwp:qt5k01s7x9. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Lisa Schiff (email available below). General contact details of provider: https://edirc.repec.org/data/itucbus.html .

    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.