Automatic Scaffolding Workface Assessment for Activity Analysis through Machine Learning
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Occupational Health and Safety Research · BIM and Construction Integration · Construction Engineering and Safety
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Design and Built Environment, Curtin University, Bently 6102, Australia
School of Built Environment, Western Sydney University, Sydney 2000, Australia
School of Architecture and Built Environment, Deakin University, Melbourne 3220, Australia
School of Electronics and Information Engineering, Tiangong University, Tianjin 300387, China
School of Civil Engineering and Architecture, East China Jiao Tong University, Nanchang 330012, China
Australasian Joint Research Centre for Building Information Modelling, Curtin University, Perth 6845, Australia
Funding
Australian Research Council
References (98)
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