IDEAS home Printed from https://ideas.repec.org/a/sae/intdis/v16y2020i6p1550147720935769.html
   My bibliography  Save this article

An Internet of Things sensor–based construction workload measurement system for construction process management

Author

Listed:
  • JunYoung Moon
  • Ahyoung Lee
  • Se Dong Min
  • Min Hong

Abstract

In this article, we adapted a sensor-based smart insole to monitor the workload of the construction material carrying work frequently occurring at the construction site. Generally, the tasks of the construction material carrying work by the construction site workers proceed through walk. Therefore, we designed and implemented an application and server to receive and calculate data from the Internet of Things sensors to automatically estimate the weight of the construction material being carried and time of these works based on the characteristic of walking. As a result of the experimental tests with 15 people using the proposed method, it was confirmed that there was a correlation between the signal change at the foot plantar pressure during walking and the weight change of the construction material carried by the workers. It was confirmed that the foot pressure value during walking can be used to estimate the weight of the construction material that the worker currently possesses. Based on this, we were able to estimate the actual weight of the object with an accuracy of 91% from the 20 new test workers, and we were able to measure the work time with an accuracy of 97%.

Suggested Citation

  • JunYoung Moon & Ahyoung Lee & Se Dong Min & Min Hong, 2020. "An Internet of Things sensor–based construction workload measurement system for construction process management," International Journal of Distributed Sensor Networks, , vol. 16(6), pages 15501477209, June.
  • Handle: RePEc:sae:intdis:v:16:y:2020:i:6:p:1550147720935769
    DOI: 10.1177/1550147720935769
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1550147720935769
    Download Restriction: no

    File URL: https://libkey.io/10.1177/1550147720935769?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
    ---><---

    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:sae:intdis:v:16:y:2020:i:6:p:1550147720935769. 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: SAGE Publications (email available below). General contact details of provider: .

    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.