Modeling a contractor's markup estimation

Min Liu, Yean Yng Ling

Research output: Contribution to journalReview articlepeer-review

62 Scopus citations

Abstract

The estimation of markup is a difficult process for contractors in a changeable and uncertain construction environment. In this study, a fuzzy logic-based artificial neural network (ANN) model, called the fuzzy neural network (FNN) model, is constructed to assist contractors in making markup decisions. With the fuzzy logic inference system integrated inside, the FNN model provides users with a clear explanation to justify the rationality of the estimated markup output. Meanwhile, with the self-learning ability of ANN, the accuracy of the estimation results is improved. From a survey and interview with local contractors, the factors that affect markup estimation and the rules applied in the markup decision are identified. Based on the finding, both ANN and FNN models were constructed and trained in different project scenarios. The comparison of the two models shows that FNN will assist contractors with markup estimation with more accurate results and convincing user-defined linguistic rules inside.

Original languageEnglish (US)
Pages (from-to)391-399
Number of pages9
JournalJournal of Construction Engineering and Management
Volume131
Issue number4
DOIs
StatePublished - Apr 2005
Externally publishedYes

Keywords

  • Bids
  • Construction management
  • Contractors
  • Fuzzy sets
  • Neural networks

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Industrial relations
  • Strategy and Management

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