Automating equipment productivity measurement using deep learning

Elham Mahamedi, Kay Rogage, Omar Doukari, Mohamad Kassem

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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.
Original languageEnglish
Title of host publicationProceedings of the 2021 European Conference on Computing in Construction
EditorsJames O'Donnell, Daniel Hall, Dragana Nikolic, Athanasios Chassiakos
PublisherUniversity College Dublin
Number of pages8
ISBN (Electronic)9783907234549
Publication statusPublished - 26 Jul 2021
Event2021 European Conference of Computing in Construction (2021 EC³) - Online Conference, Rhodes, Greece
Duration: 19 Jul 202128 Jul 2021

Publication series

NameComputing in Construction
PublisherUniversity College Dublin


Conference2021 European Conference of Computing in Construction (2021 EC³)
Abbreviated title2021 EC3
Internet address


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