Openly accessible

Automatic scaffolding workface assessment for activity analysis through machine learning

Ying, W, Shou, W, Wang, Jun, Shi, W, Sun, Y, Ji, D, Gai, H, Wang, X and Chen, M 2021, Automatic scaffolding workface assessment for activity analysis through machine learning, Applied sciences, vol. 11, no. 9, pp. 1-23, doi: 10.3390/app11094143.

Attached Files
Name Description MIMEType Size Downloads

Title Automatic scaffolding workface assessment for activity analysis through machine learning
Author(s) Ying, W
Shou, W
Wang, JunORCID iD for Wang, Jun orcid.org/0000-0001-8889-6216
Shi, W
Sun, Y
Ji, D
Gai, H
Wang, X
Chen, M
Journal name Applied sciences
Volume number 11
Issue number 9
Article ID 4143
Start page 1
End page 23
Total pages 23
Publisher MDPI AG
Place of publication Basel, Switzerland
Publication date 2021
ISSN 2076-3417
Keyword(s) Science & Technology
Physical Sciences
Technology
Chemistry, Multidisciplinary
Engineering, Multidisciplinary
Materials Science, Multidisciplinary
Physics, Applied
Chemistry
Engineering
Materials Science
Physics
scaffolding
activity analysis
workface assessment
video camera
machine learning
skeleton extraction
POSTURE ANALYSIS
CONSTRUCTION
INTEGRATION
TRANSPORT
SPACE
BIM
Summary Scaffolding serves as one construction trade with high importance. However, scaffolding suffers from low productivity and high cost in Australia. Activity Analysis is a continuous procedure of assessing and improving the amount of time that craft workers spend on one single construction trade, which is a functional method for monitoring onsite operation and analyzing conditions causing delays or productivity decline. Workface assessment is an initial step for activity analysis to manually record the time that workers spend on each activity category. This paper proposes a method of automatic scaffolding workface assessment using a 2D video camera to capture scaffolding activities and the model of key joints and skeleton extraction, as well as machine learning classifiers, were used for activity classification. Additionally, a case study was conducted and showed that the proposed method is a feasible and practical way for automatic scaffolding workface assessment.
Language eng
DOI 10.3390/app11094143
Indigenous content off
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30151657

Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 18 Abstract Views, 1 File Downloads  -  Detailed Statistics
Created: Wed, 26 May 2021, 15:19:45 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.