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 language | English |
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Title of host publication | Proceedings of the 2021 European Conference on Computing in Construction |
Editors | James O'Donnell, Daniel Hall, Dragana Nikolic, Athanasios Chassiakos |
Publisher | University College Dublin |
Pages | 140-147 |
Number of pages | 8 |
Volume | 2 |
ISBN (Electronic) | 9783907234549 |
DOIs | |
Publication status | Published - 26 Jul 2021 |
Event | 2021 European Conference of Computing in Construction (2021 EC³) - Online Conference, Rhodes, Greece Duration: 19 Jul 2021 → 28 Jul 2021 https://ec-3.org/conference2021/ |
Publication series
Name | Computing in Construction |
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Publisher | University College Dublin |
Conference
Conference | 2021 European Conference of Computing in Construction (2021 EC³) |
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Abbreviated title | 2021 EC3 |
Country/Territory | Greece |
City | Rhodes |
Period | 19/07/21 → 28/07/21 |
Internet address |