IDEAS home Printed from https://ideas.repec.org/a/taf/conmgt/v24y2006i10p1029-1044.html
   My bibliography  Save this article

Construction equipment productivity estimation using artificial neural network model

Author

Listed:
  • Seung Ok
  • Sunil Sinha

Abstract

Estimating equipment production rates is both an art and a science. An accurate prediction of the productivity of earthmoving equipment is critical for accurate construction planning and project control. Owing to the unique work requirements and changeable environment of each construction project, the influences of job and management factors on operation productivity are often very complex. Hence, construction productivity estimation, even for an operation with well-known equipment and work methods, can be challenging. This study develops and compares two methods for estimating construction productivity of dozer operations (the transformed regression analysis, and a non-linear analysis using neural network model). It is the hypothesis of this study that the proposed neural networks model may improve productivity estimation models because of the neural network's inherent ability to capture non-linearity and the complexity of the changeable environment of each construction project. The comparison of results suggests that the non-linear artificial neural network (ANN) has the potential to improve the equipment productivity estimation model.

Suggested Citation

  • Seung Ok & Sunil Sinha, 2006. "Construction equipment productivity estimation using artificial neural network model," Construction Management and Economics, Taylor & Francis Journals, vol. 24(10), pages 1029-1044.
    • Handle: RePEc:taf:conmgt:v:24:y:2006:i:10:p:1029-1044
    DOI: 10.1080/01446190600851033
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/01446190600851033
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/01446190600851033?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.

    Citations

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


    Cited by:

    1. Dixit Saurav, 2021. "Impact of management practices on construction productivity in Indian building construction projects: an empirical study," Organization, Technology and Management in Construction, Sciendo, vol. 13(1), pages 2383-2390, January.
    2. Mohd. Ahmed & Saeed AlQadhi & Javed Mallick & Nabil Ben Kahla & Hoang Anh Le & Chander Kumar Singh & Hoang Thi Hang, 2022. "Artificial Neural Networks for Sustainable Development of the Construction Industry," Sustainability, MDPI, vol. 14(22), pages 1-21, November.
    3. Roy, Adrien & McCabe, Brenda Y. & Saxe, Shoshanna & Posen, I. Daniel, 2024. "Review of factors affecting earthworks greenhouse gas emissions and fuel use," Renewable and Sustainable Energy Reviews, Elsevier, vol. 194(C).
    4. Hassanean S. H. Jassim & Weizhuo Lu & Thomas Olofsson, 2017. "Predicting Energy Consumption and CO 2 Emissions of Excavators in Earthwork Operations: An Artificial Neural Network Model," Sustainability, MDPI, vol. 9(7), pages 1-25, July.
    5. Martina Šopić & Mladen Vukomanović & Diana Car-Pušić, 2024. "Machine Cost-Effectiveness in Earthworks: Early Warning System and Status of the Previous Work Period," Sustainability, MDPI, vol. 16(17), pages 1-19, August.

    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:24:y:2006:i:10:p:1029-1044. 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.