@inproceedings{c2f1465462124f82b2263af33cea7819,
title = "Automating equipment productivity measurement using deep learning",
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.",
keywords = "Excavators, Productivity, IMU, Machine Learning",
author = "Elham Mahamedi and Kay Rogage and Omar Doukari and Mohamad Kassem",
year = "2021",
month = jul,
day = "26",
doi = "10.35490/EC3.2021.153",
language = "English",
volume = "2",
series = "Proceedings of the European Conference on Computing in Construction",
publisher = "European Council on Computing in Construction (EC3)",
pages = "140--147",
editor = "Hall, {Daniel M.} and Athanasios Chassiakos and James O'Donnell and Dragana Nikolic and Yiannis Xenides",
booktitle = "Proceedings of the 2021 European Conference on Computing in Construction",
note = "European Conference on Computing in Construction, EC3 2021 ; Conference date: 26-07-2021 Through 28-07-2021",
}