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Journal of Computing in Civil Engineering / Volume 22 / Issue 3 / TECHNICAL PAPERS

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Assessing Residual Value of Heavy Construction Equipment Using Predictive Data Mining Model

J. Comput. Civ. Eng. 22, 181 (2008); doi:10.1061/(ASCE)0887-3801(2008)22:3(181) (11 pages)

Hongqin Fan1, Simaan AbouRizk2, Hyoungkwan Kim3, and Osmar Zaïane4

1Graduate Research Assistant, Dept. of Civil and Environmental Engineering, Univ. of Alberta, Edmonton AB T6G 2W2, Canada. E-mail: hfan@ualberta.ca
2Chair, NSERFC/Alberta Construction Industry Research Chair, Professor, Dept. of Civil and Environmental Engineering, Univ. of Alberta, Edmonton AB T6G 2W2, Canada. E-mail: abouRizk@ualberta.ca
3Assistant Professor, Dept. of Civil and Environmental Engineering, Yonsei Univ., Seoul 120-749 (corresponding author). E-mail: hyoungkwan@yonsei.ac.kr
4Associate Professor, Dept. of Computing Science, Univ. of Alberta, Edmonton AB T6G 2E8, Canada. E-mail: zaiane@cs.ualberta.ca

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(Submitted 5 April 2007; accepted 26 June 2007)

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-of-thumb 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.

© 2008 American Society of Civil Engineers

Acknowledgments

This work was supported by the Natural Sciences and Engineering Research Council of Canada (Grant No. CRD 226956-99) and Yonsei Research Grant. The writers wish to express their sincere appreciation to Dale Tillapaugh, for his participation and contribution to this research.

Article Outline

  1. Introduction
  2. Literature Review
  3. Data Mining for Prediction of Equipment Residual Value
  4. AutoRegressive Tree Algorithm
  5. ART Model for Prediction of Equipment Residual Value
    1. Data Sources
    2. Feature Selection
    3. Data Quality Control
    4. Model Generation and Validation
  6. Comparison of ART with ANN and Multivariate Linear Regression Models
  7. Deployment of Predictive Data Mining Models for Equipment Residual Value
  8. Discussions
  9. Summary and Conclusions

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http://dx.doi.org/10.1061/(ASCE)0887-3801(2008)22:3(181)

PUBLICATION DATA

ISSN:

0887-3801 (print)  
1943-5487 (online)

Publisher:

American Society of Civil Engineers

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