IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i11p4127-d181819.html
   My bibliography  Save this article

An Innovative Method for Project Transaction Mode Design Based on Case-Based Reasoning: A Chinese Case Study

Author

Listed:
  • Jiyong Ding

    (Institute of Engineering Management, Business School of Hohai University, Nanjing 211100, China
    Jiangsu Provincial Collaborative Innovation Center of World Water Valley and Water Ecological Civilization, Nanjing 211100, China)

  • Jianyao Jia

    (School of Economics and Management, Tongji University, Shanghai 200092, China)

  • Chenhao Jin

    (Institute of Engineering Management, Business School of Hohai University, Nanjing 211100, China
    Jiangsu Provincial Collaborative Innovation Center of World Water Valley and Water Ecological Civilization, Nanjing 211100, China)

  • Na Wang

    (Institute of Engineering Management, Business School of Hohai University, Nanjing 211100, China)

Abstract

Aiming at the design of a project transaction mode, the case-based reasoning (CBR) method is used as a methodology to build a case-based reasoning system based on project performance predictions. Thirty-four cases are initially selected for the practical application. Based on the classical CBR, the performance forecast is added, an improved continuous variable interpolation scoring method is proposed, and three types of manual revision methods are proposed: owner’s preference for the project transaction mode, extreme value, and secondary learning. The innovative method is verified with Nanjing HF Project as an example, and the results show that the case-based reasoning system can optimize the selection and design of the project transaction mode, providing a certain guarantee for project performance and facilitating the transfer of construction experience and knowledge within the construction industry.

Suggested Citation

  • Jiyong Ding & Jianyao Jia & Chenhao Jin & Na Wang, 2018. "An Innovative Method for Project Transaction Mode Design Based on Case-Based Reasoning: A Chinese Case Study," Sustainability, MDPI, vol. 10(11), pages 1-18, November.
  • Handle: RePEc:gam:jsusta:v:10:y:2018:i:11:p:4127-:d:181819
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/11/4127/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/10/11/4127/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Nahyun Kwon & Joosung Lee & Moonsun Park & Inseok Yoon & Yonghan Ahn, 2019. "Performance Evaluation of Distance Measurement Methods for Construction Noise Prediction Using Case-Based Reasoning," Sustainability, MDPI, vol. 11(3), pages 1-18, February.

    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:10:y:2018:i:11:p:4127-:d:181819. 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: 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.