Automating equipment productivity measurement using deep learning

Elham Mahamedi, Kay Rogage, Omar Doukari, Mohamad Kassem

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)
40 Downloads (Pure)

Abstract

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
EditorsDaniel M. Hall, Athanasios Chassiakos, James O'Donnell, Dragana Nikolic, Yiannis Xenides
PublisherEuropean Council on Computing in Construction (EC3)
Pages140-147
Number of pages8
Volume2
ISBN (Electronic)9783907234549
DOIs
Publication statusPublished - 26 Jul 2021
EventEuropean Conference on Computing in Construction, EC3 2021 - Virtual, Online
Duration: 26 Jul 202128 Jul 2021

Publication series

NameProceedings of the European Conference on Computing in Construction
ISSN (Electronic)2684-1150

Conference

ConferenceEuropean Conference on Computing in Construction, EC3 2021
CityVirtual, Online
Period26/07/2128/07/21

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