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Estimating residual value of heavy construction equipment using ensemble learning

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2021
bitstream_9567.pdf (2.791Mb)
Authors
Milošević, Igor
Kovačević, Miloš A.
Petronijević, Predrag
Article (Accepted Version)
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Abstract
Knowing the right moment for the sale of used heavy construction equipment is important information for every construction company. The proposed methodology uses ensemble machine learning techniques to estimate the price (residual value) of used heavy equipment in both the present and the near future. Each machine in the model is represented with four groups of attributes: age and mechanical (describing the machine) and geographical and economic (describing the target market). The research suggests that the ensemble model based on random forest, light gradient boosting, and neural network members, as well as support vector regression as a decision unit, gives better estimates than the traditional regression or individual machine learning models. The model is built and verified on a large data set of 500,000 machines advertised in 50 US states from 1989 to 2012.
Keywords:
Artificial intelligence and machine learning / Business management / Decision making / Quantitative methods
Source:
Journal of Construction Engineering and Management. ASCE / American Society of Civil Engineers, 2021, 147, 7
Publisher:
  • http://cedb.asce.org

DOI: 10.1061/(ASCE)CO.1943-7862.0002088

ISSN: 0733-9364

WoS: 000656447700011

[ Google Scholar ]
URI
https://grafar.grf.bg.ac.rs/handle/123456789/2422
Collections
  • Катедра за управљање пројектима у грађевинарству
  • Radovi istraživača / Researcher's publications
Institution/Community
GraFar
TY  - JOUR
AU  - Milošević, Igor
AU  - Kovačević, Miloš A.
AU  - Petronijević, Predrag
PY  - 2021
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/2422
AB  - Knowing the right moment for the sale of used heavy construction equipment is important information for every construction company. The proposed methodology uses ensemble machine learning techniques to estimate the price (residual value) of used heavy equipment in both the present and the near future. Each machine in the model is represented with four groups of attributes: age and mechanical (describing the machine) and geographical and economic (describing the target market). The research suggests that the ensemble model based on random forest, light gradient boosting, and neural network members, as well as support vector regression as a decision unit, gives better estimates than the traditional regression or individual machine learning models. The model is built and verified on a large data set of 500,000 machines advertised in 50 US states from 1989 to 2012.
PB  - http://cedb.asce.org
T2  - Journal of Construction Engineering and Management. ASCE / American Society of Civil Engineers
T1  - Estimating residual value of heavy construction equipment using ensemble learning
IS  - 7
VL  - 147
DO  - 10.1061/(ASCE)CO.1943-7862.0002088
ER  - 
@article{
author = "Milošević, Igor and Kovačević, Miloš A. and Petronijević, Predrag",
year = "2021",
abstract = "Knowing the right moment for the sale of used heavy construction equipment is important information for every construction company. The proposed methodology uses ensemble machine learning techniques to estimate the price (residual value) of used heavy equipment in both the present and the near future. Each machine in the model is represented with four groups of attributes: age and mechanical (describing the machine) and geographical and economic (describing the target market). The research suggests that the ensemble model based on random forest, light gradient boosting, and neural network members, as well as support vector regression as a decision unit, gives better estimates than the traditional regression or individual machine learning models. The model is built and verified on a large data set of 500,000 machines advertised in 50 US states from 1989 to 2012.",
publisher = "http://cedb.asce.org",
journal = "Journal of Construction Engineering and Management. ASCE / American Society of Civil Engineers",
title = "Estimating residual value of heavy construction equipment using ensemble learning",
number = "7",
volume = "147",
doi = "10.1061/(ASCE)CO.1943-7862.0002088"
}
Milošević, I., Kovačević, M. A.,& Petronijević, P.. (2021). Estimating residual value of heavy construction equipment using ensemble learning. in Journal of Construction Engineering and Management. ASCE / American Society of Civil Engineers
http://cedb.asce.org., 147(7).
https://doi.org/10.1061/(ASCE)CO.1943-7862.0002088
Milošević I, Kovačević MA, Petronijević P. Estimating residual value of heavy construction equipment using ensemble learning. in Journal of Construction Engineering and Management. ASCE / American Society of Civil Engineers. 2021;147(7).
doi:10.1061/(ASCE)CO.1943-7862.0002088 .
Milošević, Igor, Kovačević, Miloš A., Petronijević, Predrag, "Estimating residual value of heavy construction equipment using ensemble learning" in Journal of Construction Engineering and Management. ASCE / American Society of Civil Engineers, 147, no. 7 (2021),
https://doi.org/10.1061/(ASCE)CO.1943-7862.0002088 . .

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