Assessing residual value of heavy construction equipment using predictive data mining model

Hongqin Fan, Simaan AbouRizk, Hyoungkwan Kim, Osmar Zaiane

Research output: Journal article publicationJournal articleAcademic researchpeer-review

30 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)181-191
Number of pages11
JournalJournal of Computing in Civil Engineering
Issue number3
Publication statusPublished - 5 May 2008
Externally publishedYes


  • Construction equipment
  • Data analysis
  • Decision making
  • Information management
  • Knowledge-based systems

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Computer Science Applications
  • Computational Theory and Mathematics

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