Automatic Scaffolding Workface Assessment for Activity Analysis through Machine Learning

Applied Sciences

Jun Wang¹  Wenchi Shou²  Xiangyu Wang³  Mengcheng Chen³  Weixiang Shi⁴  Dazhi Ji⁴  Wenzheng Ying⁴  Yanhui Sun⁴  Haoxuan Gai⁵ 

[1] School of Architecture and Built Environment, Deakin University, Melbourne 3220, Australia;School of Built Environment, Western Sydney University, Sydney 2000, Australia;School of Civil Engineering and Architecture, East China Jiao Tong University, Nanchang 330012, China;School of Design and Built Environment, Curtin University, Bently 6102, Australia;School of Electronics and Information Engineering, TianGong University, Tianjin 300387, China;

关键词: scaffolding;    activity analysis;    workface assessment;    video camera;    machine learning;    skeleton extraction;   

DOI  :  10.3390/app11094143

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown