Comparison of construction cost estimating models based on regression analysis, neural networks, and case-based reasoning

Gwang Hee Kim, Sung Hoon An, Kyung In Kang

Research output: Contribution to journalArticle

250 Citations (Scopus)

Abstract

Adequate estimation of construction costs is a key factor in construction projects. This paper examines the performance of three cost estimation models. The examinations are based on multiple regression analysis (MRA), neural networks (NNs), and case-based reasoning (CBR) of the data of 530 historical costs. Although the best NN estimating model gave more accurate estimating results than either the MRA or the CBR estimating models, the CBR estimating model performed better than the NN estimating model with respect to long-term use, available information from result, and time versus accuracy tradeoffs.

Original languageEnglish
Pages (from-to)1235-1242
Number of pages8
JournalBuilding and Environment
Volume39
Issue number10
DOIs
Publication statusPublished - 2004 Oct

Keywords

  • Case-based reasoning
  • Cost estimation
  • Multiple regression analysis
  • Neural networks

ASJC Scopus subject areas

  • Environmental Engineering
  • Civil and Structural Engineering
  • Geography, Planning and Development
  • Building and Construction

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