Determining attribute weights in a CBR model for early cost prediction of structural systems

Dogan S. Z., Arditi D., Guenaydin H. M.

JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT, vol.132, no.10, pp.1092-1098, 2006 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 132 Issue: 10
  • Publication Date: 2006
  • Doi Number: 10.1061/(asce)0733-9364(2006)132:10(1092)
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1092-1098
  • Keywords: construction costs, cost estimates, decision making, decision support systems, spreadsheets, optimization models, predictions
  • Istanbul Technical University Affiliated: No


This paper compares the performance of three optimization techniques, namely feature counting, gradient descent, and genetic algorithms (GA) in generating attribute weights that were used in a spreadsheet-based case based reasoning (CBR) prediction model. The generation of the attribute weights by using the three optimization techniques and the development of the procedure used in the CBR model are described in this paper in detail. The model was tested by using data pertaining to the early design parameters and unit cost of the structural system of 29 residential building projects. The results indicated that GA-augmented CBR performed better than CBR used in association with the other two optimization techniques. The study is of benefit primarily to researchers as it compares the impact attribute weights generated by three different optimization techniques on the performance of a CBR prediction tool.