Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/2186
Title: Determining attribute weights in a CBR model for early cost prediction of structural systems
Authors: Doğan, Sevgi Zeynep
Arditi, David
Günaydın, Hüsnü Murat
Doğan, Sevgi Zeynep
Günaydın, Hüsnü Murat
Izmir Institute of Technology. Architecture
Keywords: Structural design
Construction costs
Cost estimates
Decision making
Issue Date: 2006
Publisher: American Society of Civil Engineers (ASCE)
Source: Doğan, S. Z., Arditi, D., and Günaydın, H. M. (2006). Determining attribute weights in a CBR model for early cost prediction of structural systems. Journal of Construction Engineering and Management, 132(10), 1092-1098. doi:10.1061/(ASCE)0733-9364(2006)132:10(1092)
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.
URI: http://doi.org/10.1061/(ASCE)0733-9364(2006)132:10(1092)
http://hdl.handle.net/11147/2186
ISSN: 0733-9364
0733-9364
Appears in Collections:Architecture / Mimarlık
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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