Automating equipment productivity measurement using deep learning

Mahamedi, Elham, Rogage, Kay, Doukari, Omar and Kassem, Mohamad (2021) Automating equipment productivity measurement using deep learning. In: Proceedings of the 2021 European Conference on Computing in Construction. Computing in Construction, 2 . University College Dublin, Dublin, pp. 140-147. ISBN 9783907234549

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Measuring the productivity of earth moving equipment help to identify their inefficiencies and improve their performance; however, measurement processes are time and resource intensive. Current literature has foccussed on automating equipment activity capture but still lack adequate approaches for measurement of equipment productivity rates. Our contribution is to present a methodology for automating equipment productivity measurement using kinematic and noise data collected through smartphone sensors from within equipment and deep learning algorithms for recognizing equipment states. The testing of the proposed method in a real world case study demonstrated very high accuracy of 99.78 in measuring productivity of an excavator.

Item Type: Book Section
Additional Information: Excavators, Productivity, IMU, Machine Learning.
Subjects: G400 Computer Science
H200 Civil Engineering
H300 Mechanical Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Faculties > Engineering and Environment > Mechanical and Construction Engineering
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Depositing User: Elena Carlaw
Date Deposited: 13 Apr 2022 12:25
Last Modified: 13 Apr 2022 12:30

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