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Determining Attribute Weights in a CBR Model for Early Cost Prediction of Structural Systems |
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| Authors: | Sevgi Zeynep Do?an David Arditi H. Murat Günayd?n |
| Affiliation: | 1Visiting Research Scholar, Dept. of Civil and Architectural Engineering, Illinois Institute of Technology, Chicago IL 60616. E-mail: dogan@iit.edu 2Professor, Dept. of Civil and Architectural Engineering, Illinois Institute of Technology, Chicago, IL 60616. E-mail: arditi@iit.edu 3Associate Professor, Dept. of Architecture, Izmir Institute of Technology, Izmir, Turkey. |
| Abstract: | 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. |
| Keywords: | Construction costs Cost estimates Decision making Decision support systems Spreadsheets Optimization models Predictions |