Estimating Residual Value of Heavy Construction Equipment Using Ensemble Learning
Publication: Journal of Construction Engineering and Management
Volume 147, Issue 7
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.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
References
Bayzid, S. M., Y. Mohamed, and M. Al-Hussein. 2016. “Prediction of maintenance cost for road construction equipment: A case study.” Can. J. Civ. Eng. 43 (5): 480–492. https://doi.org/10.1139/cjce-2014-0500.
Breiman, L. 2001. “Random forests.” Mach. Learn. 45 (1): 5–32. https://doi.org/10.1023/A:1010933404324.
Breiman, L., J. Friedman, C. Stone, and L. A. Olshen. 1984. Classification and regression trees. Oxfordshire, UK: Taylor and Francis.
Catepillar. 2019. “Annual report 2018.” Accessed February 14, 2019. https://d18rn0p25nwr6d.cloudfront.net/CIK-0000018230/5a8a4052-339a-4b85-ac80-0193d0419386.pdf.
Chou, J. S., and C. Lin. 2013. “Predicting disputes in public-private partnership projects: Classification and ensemble models.” J. Comput. Civ. Eng. 27 (1): 51–60. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000197.
Cross, T. L., and G. M. Perry. 1995. “Depreciation patterns for agricultural machinery.” Am. J. Agric. Econ. 77 (1): 194–204. https://doi.org/10.2307/1243901.
Cubbage, F. W., J. A. Burgess, and B. J. Stokes. 1991. “Cross-sectional estimates of logging equipment resale values.” For. Prod. J. 41 (10): 16–22.
Drucker, H., C. Burges, L. Kaufman, A. Smola, and V. Vapnik. 1997. “Support vector regression machines.” Adv. Neural Inf. Process. Syst. 9 (May): 155–161.
Elmousalami, H. H. 2020. “Artificial intelligence and parametric construction cost estimate modeling: State-of-the-art review.” J. Constr. Eng. Manage. 146 (1): 03119008 https://doi.org/10.1061/(ASCE)CO.1943-7862.0001678.
Fan, H., S. AbouRizk, H. Kim, and O. Zaïane. 2008. “Assessing residual value of heavy construction equipment using predictive data mining model.” J. Comput. Civ. Eng. 22 (3): 181–191. https://doi.org/10.1061/(ASCE)0887-3801(2008)22:3(181).
Guyon, I., J. Weston, S. Barnhill, and V. Vapnik. 2002. “Gene selection for cancer classification using support vector machines.” Mach. Learn. 46 (1–3): 389–422. https://doi.org/10.1023/A:1012487302797.
Hansen, J., and R. Nelson. 2002. “Data mining of time series using stacked generalizers.” Neurocomputing 43 (1–4): 173–184. https://doi.org/10.1016/S0925-2312(00)00364-7.
Indrė, Ž. 2014. “Learning under concept drift: An overview.” In Faculty of mathematics and informatics. Vilnius, Lithuania: Vilnius Univ.
James, W., and M. Waleed. 2005. “Adverse selection and reputation systems in online auctions: Evidence from eBay motors.” In Proc., Int. Conf. on Information Systems, 847–858. Berlin: Springer.
Kastens, T. H. 2002. An owning and operating cost calculator model for construction equipment. Blacksburg, VA: Virginia Polytechnic Institute.
Ke, G., Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T.-Y. Liu. 2017. “LightGBM: A highly efficient gradient boosting decision tree.” In Proc., Neural Information Processing Systems. La Jolla, CA: Neural Information Processing Systems Foundation.
Lucko, G. 2003. “A statistical analysis and model of the residual value of different types of heavy construction equipment.” Accessed December 3, 2003. http://Scholar.Lib.vt.Edu/Theses/Available/Etd-12032003-122642.
Lucko, G. 2011. “Modeling the residual market value of construction equipment under changed economic conditions.” J. Constr. Eng. Manage. 137 (10): 806–816. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000279.
Lucko, G., C. M. Anderson-Cook, and M. C. Vorster. 2006. “Statistical considerations for predicting residual value of heavy equipment.” J. Constr. Eng. Manage. 132 (7): 723–732. https://doi.org/10.1061/(ASCE)0733-9364(2006)132:7(723).
Lucko, G., and Z. W. Mitchell. 2010. “Quantitative research: Preparation of incongruous economic data sets for archival data analysis.” J. Constr. Eng. Manage. 136 (1): 49–57. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000078.
Lucko, G., and M. C. Vorster. 2003. “Predicting the residual value of heavy construction equipment.” In Towards a vision for information technology in civil engineering, 1–11. Reston, VA: ASCE. https://doi.org/10.1061/40704(2003)49.
Lucko, G., M. C. Vorster, and C. M. Anderson-Cook. 2007. “Unknown element of owning costs—Impact of residual value.” J. Constr. Eng. Manage. 133 (1): 3–9. https://doi.org/10.1061/(ASCE)0733-9364(2007)133:1(3).
Milosevic, I. 2020. “OSF project.” Accessed November 13, 2020. https://doi.org/doi/10.17605/OSF.IO/VMBR2.
Milosevic, I., P. Petronijevic, and D. Arizanovic. 2020. “Determination of residual value of construction machinery based on machine age.” Gradevinar 72 (1): 45–55. https://doi.org/10.14256/JCE.1285.2015.
Mitchell, Z., J. Hildreth, and M. Vorster. 2011. “Using the cumulative cost model to forecast equipment repair costs: Two different methodologies.” J. Constr. Eng. Manage. 137 (10): 817–822. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000302.
Pedregosa, F., et al. 2011. “Scikit-learn: Machine learning in Python.” J. Mach. Learn. Res. 12 (2): 2825–2830.
Pitroda, J., and V. Chetna. 2015. “A critical literature review on comparative analysis of construction equipments—Rent and buy.” J. Int. Acad. Res. Multidiscip. 2 (2320–5083): 130–141.
Ripley, B. 1996. Pattern recognition and neural networks. Cambridge, UK: Cambridge University Press.
Scholz, M., and R. Klinkenberg. 2005. “An ensemble classifier for drifting concepts.” In Proc., 2nd Int. Workshop on Knowledge Discovery in Data Streams. Dortmund, Germany: Technische Universitat Dortmund.
Stojadinović, Z. 2018. Claims on construction projects: Quantification and prevention, Conference. Newtown Square, PA: Project Management Institute.
Tang, F., and H. Ishwaran. 2017. “Random forest missing data algorithms.” Stat. Anal. Data Min. 10 (6): 363–377. https://doi.org/10.1002/sam.11348.
Tianfeng, C., and R. Draxler. 2014. “Root mean square error (RMSE) or mean absolute error (MAE)?—Arguments against avoiding RMSE in the literature.” Geosci. Model Dev. 7 (3): 1247–1250.
Unterschultz, J., and G. Mumey. 1996. “Reducing investment risk in tractors and combines with improved terminal asset value forecasts.” Can. J. Agric. Econ. 44 (3): 295–309. https://doi.org/10.1111/j.1744-7976.1996.tb00152.x.
US Board of Governors of the Federal Reserve System. 2019. “Industrial production index (INDPRO).” Accessed March 16, 2021. https://fred.stlouisfed.org/series/INDPRO.
Windeatt, T., and A. Gholamreza. 2004. “Decision tree simplification for classifier ensembles.” Int. J. Pattern Recognit. Artif. Intell. 18 (5): 749–776. https://doi.org/10.1142/S021800140400340X.
Wolpert, D. 1992. “Stacked generalization.” Neural Networks 5 (2): 241–259. https://doi.org/10.1016/S0893-6080(05)80023-1.
Zhi-Hua, Z. 2012. Ensemble methods—Foundations and algorithms. Boca Raton, FL: CRC Press.
Zong, Y. 2017. Maintenance cost and residual value prediction of heavy construction equipment. Edmonton, Canada: Univ. of Alberta. https://doi.org/10.7939/R3XS5JV59.
Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 147 • Issue 7 • July 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Aug 19, 2020
Accepted: Feb 2, 2021
Published online: May 13, 2021
Published in print: Jul 1, 2021
Discussion open until: Oct 13, 2021
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