Assessing Residual Value of Heavy Construction Equipment Using Prediuctive Data Mining Model
Construction equipment constitutes a significant portion of investment in fixed assets by large contractors. To make the right
decisions on equipment repair, rebuilding, disposal, or equipment fleet optimization to maximize the return of investment, the contractors
need to predict the residual value of heavy construction equipment to an acceptable level of accuracy. Current practice of using rule-ofthumb
or statistical regression methods cannot satisfactorily capture the dynamic relationship between the residual value of a piece of
heavy equipment and its influencing factors, and such rules or models are difficult to integrate into a decision support system. This paper
introduces a data mining based approach for estimating the residual value of heavy construction equipment using a predictive data mining
model, and its potential benefits on the decision making of construction equipment management. Compared to the current practice of
assessing equipment residual values, the proposed approach demonstrates advantages of ease of use, better interpretability, and adequate
accuracy.
Citation
H. Fan,
S. Abourizk,
H. Kim,
O. Zaiane.
"Assessing Residual Value of Heavy Construction Equipment Using Prediuctive Data Mining Model". ASCE Journal of Computing in Civil Engineering, 22(3), pp 181-191, May 2008.
| Keywords: |
Civil Engineering, data mining application |
| Category: |
In Journal |
BibTeX
@article{Fan+al:08,
author = {Hongqin Fan and Simaan Abourizk and Hyoungkwan Kim and Osmar R.
Zaiane},
title = {Assessing Residual Value of Heavy Construction Equipment Using
Prediuctive Data Mining Model},
Volume = "22",
Number = "3",
Pages = {181-191},
journal = {ASCE Journal of Computing in Civil Engineering},
year = 2008,
}Last Updated: August 17, 2009
Submitted by Osmar Zaiane 
