TY - GEN
T1 - Equipment data-based activity recognition of construction machinery
AU - Fischer, Anne
AU - Bedrikow, Alexandre Beiderwellen
AU - Kessler, Stephan
AU - Fottner, Johannes
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/6/21
Y1 - 2021/6/21
N2 - Heavy equipment plays an essential role in construction projects. However, to optimize the allocation of that resource (e.g., via discrete-event simulation), the correct monitoring of the current usage of the equipment is imperative. Machine learning methods are usually based on the mounting of additional sensors such as accelerometers or gyroscopes on the equipment. However, many kinds of equipment already transmit production data to a proprietary platform. This paper explores the possibilities of performing activity recognition based on already available equipment data transmitted by the equipment, without the need for additional sensors. The proposed approach is based on hybrid models consisting of convolutional neural networks (CNN) and recurrent neural networks (RNN). Additionally, the influence of the use of bidirectional RNN is investigated. The hybrid models are compared with other baseline models. Both hybrid models demonstrate good results compared to the baseline models. Furthermore, the influence of the sample size is examined.
AB - Heavy equipment plays an essential role in construction projects. However, to optimize the allocation of that resource (e.g., via discrete-event simulation), the correct monitoring of the current usage of the equipment is imperative. Machine learning methods are usually based on the mounting of additional sensors such as accelerometers or gyroscopes on the equipment. However, many kinds of equipment already transmit production data to a proprietary platform. This paper explores the possibilities of performing activity recognition based on already available equipment data transmitted by the equipment, without the need for additional sensors. The proposed approach is based on hybrid models consisting of convolutional neural networks (CNN) and recurrent neural networks (RNN). Additionally, the influence of the use of bidirectional RNN is investigated. The hybrid models are compared with other baseline models. Both hybrid models demonstrate good results compared to the baseline models. Furthermore, the influence of the sample size is examined.
KW - Bidirectional RNN
KW - Convolutional neural networks (CNN)
KW - Heavy equipment
KW - Hybrid machine learning models
KW - Recurrent neural networks (RNN)
UR - http://www.scopus.com/inward/record.url?scp=85119080554&partnerID=8YFLogxK
U2 - 10.1109/ICE/ITMC52061.2021.9570272
DO - 10.1109/ICE/ITMC52061.2021.9570272
M3 - Conference contribution
AN - SCOPUS:85119080554
T3 - 2021 IEEE International Conference on Engineering, Technology and Innovation, ICE/ITMC 2021 - Proceedings
BT - 2021 IEEE International Conference on Engineering, Technology and Innovation, ICE/ITMC 2021 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Engineering, Technology and Innovation, ICE/ITMC 2021
Y2 - 21 June 2021 through 23 June 2021
ER -
