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https://hdl.handle.net/11147/2186
Başlık: | Determining attribute weights in a CBR model for early cost prediction of structural systems | Yazarlar: | Doğan, Sevgi Zeynep Arditi, David Günaydın, Hüsnü Murat |
Anahtar kelimeler: | Structural design Construction costs Cost estimates Decision making |
Yayıncı: | American Society of Civil Engineers (ASCE) | Kaynak: | 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) | Özet: | 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 |
Koleksiyonlarda Görünür: | Architecture / Mimarlık Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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