IDEAS home Printed from https://ideas.repec.org/a/taf/conmgt/v17y1999i1p91-98.html
   My bibliography  Save this article

Inducing rules for selecting retaining wall systems

Author

Listed:
  • Nie-Jia Yau
  • Jyh-Bin Yang
  • Ting-Ya Hsieh

Abstract

Rule induction is a paradigm of machine learning that governs how knowledge is acquired from experience. This paradigm not only classifies existing data into logical sets, but also expresses them by 'if-then' rules. Rule induction can be applied to the experience-oriented construction industry. A typical example would be to select an appropriate retaining wall system at the project planning stage, in which engineers normally employ certain empirical laws or select from the types for which they have relevant expertise in making appropriate selections. This work presents a novel rule induction approach, capable of inducing from 254 retaining wall cases in engineering design reports into 181 rules, thereby allowing for an appropriate retaining wall system to be selected. A system referred to herein as RULES is also constructed with an illustrative example provided as well. Test results of the system demonstrate that the rule induction approach can effectively resolve retaining wall selection problems at the project planning stage. The approach proposed herein is highly promising for resolving experience-oriented problems in the construction industry.

Suggested Citation

  • Nie-Jia Yau & Jyh-Bin Yang & Ting-Ya Hsieh, 1999. "Inducing rules for selecting retaining wall systems," Construction Management and Economics, Taylor & Francis Journals, vol. 17(1), pages 91-98.
    • Handle: RePEc:taf:conmgt:v:17:y:1999:i:1:p:91-98
    DOI: 10.1080/014461999371853
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/014461999371853
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/014461999371853?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:taf:conmgt:v:17:y:1999:i:1:p:91-98. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RCME20 .

    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.