Over the past decades, the construction industry lags further and further behind the manufacturing sector when productivity is considered. Current practices for managing labour productivity are labour intensive, time and cost consuming and error prone. They are mainly reactive processes initiated after the detection of a negatively influencing factor. Although research studies have been performed towards leveraging these limitations, a gap still exists in managing the labour productivity of multiple workers at the same time accurately, unobtrusively and cost and time efficiently. This paper proposes a trajectory-based method to address this gap. Firstly, a computer vision-based method extracts the three-dimensional (3D) trajectories of workers. Secondly, a clustering-based method converts the 3D trajectories into work cycles that depict the labour input. The proposed method features an accuracy of 84% in terms of inferring the total labour time spent on construction-related tasks – that is, direct work.
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7 September 2020
Research Article|
September 07 2020
Monitoring construction labour productivity by way of a smart technology approach Available to Purchase
Eirini Konstantinou, PhD
;
Department of Engineering, Laing O’Rourke Centre, Cambridge, UK
(corresponding author: eirinikonstant@gmail.com)
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Ioannis Brilakis, PhD
Ioannis Brilakis, PhD
Department of Engineering, Laing O’Rourke Centre, Cambridge, UK
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(corresponding author: eirinikonstant@gmail.com)
Publisher: Emerald Publishing
Received:
May 25 2020
Accepted:
August 14 2020
ICE Publishing: All rights reserved
2019
Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction (2020) 172 (2): 70–82.
Article history
Received:
May 25 2020
Accepted:
August 14 2020
Citation
Konstantinou E, Brilakis I (2020), "Monitoring construction labour productivity by way of a smart technology approach". Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction, Vol. 172 No. 2 pp. 70–82, doi: https://doi.org/10.1680/jsmic.20.00014
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