IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-981-15-8892-1_67.html
   My bibliography  Save this book chapter

Implementing Proactive Building Asset Management Through Deterioration Prediction: A Case Study in Australia

In: Proceedings of the 24th International Symposium on Advancement of Construction Management and Real Estate

Author

Listed:
  • Wenkai Luo

    (RMIT University)

  • Guomin Zhang

    (RMIT University)

  • Huu Dung Tran

    (RMIT University)

  • Sujeeva Setunge

    (RMIT University)

  • Lei Hou

    (RMIT University)

Abstract

Buildings are critical assets in most owner’s asset packages. Maintaining and rehabilitating buildings under various constraints is a crucial challenge for most owners due to the complexity of building components and the uncertainty of their deteriorations. The Markov process based probabilistic approach is an effective solution to develop an asset deterioration prediction model helping owner implementing proactive building asset management. This paper takes one Australian city council as an example to discuss the benefits of an asset deterioration prediction model to the proactive building management and explain how to calibrate and validate the Markov transition matrices from the chronologic discrete assets condition datasets. Two identical findings are that curves derived from the short-period dataset are steeper than that from the long-period dataset, and C2, C3 curves derived from the short-period dataset are not significant.

Suggested Citation

  • Wenkai Luo & Guomin Zhang & Huu Dung Tran & Sujeeva Setunge & Lei Hou, 2021. "Implementing Proactive Building Asset Management Through Deterioration Prediction: A Case Study in Australia," Springer Books, in: Gui Ye & Hongping Yuan & Jian Zuo (ed.), Proceedings of the 24th International Symposium on Advancement of Construction Management and Real Estate, pages 951-965, Springer.
    • Handle: RePEc:spr:sprchp:978-981-15-8892-1_67
    DOI: 10.1007/978-981-15-8892-1_67
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:sprchp:978-981-15-8892-1_67. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.