Architecture / Mimarlık

Permanent URI for this collectionhttps://hdl.handle.net/11147/24

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Author: search.filters.author.Doğan, Sevgi ZeynepEnd date: 2009

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Now showing 1 - 3 of 3
  • Article
    Citation - WoS: 66
    Citation - Scopus: 76
    Using Decision Trees for Determining Attribute Weights in a Case-Based Model of Early Cost Prediction
    (American Society of Civil Engineers (ASCE), 2008) Doğan, Sevgi Zeynep; Arditi, David; Günaydın, Hüsnü Murat
    This paper compares the performance of three different decision-tree-based methods of assigning attribute weights to be used in a case-based reasoning (CBR) prediction model. The generation of the attribute weights is performed by considering the presence, absence, and the positions of the attributes in the decision tree. This process and the development of the CBR simulation model are described in the paper. The model was tested by using data pertaining to the early design parameters and unit cost of the structural system of residential building projects. The CBR results indicate that the attribute weights generated by taking into account the information gain of all the attributes performed better than the attribute weights generated by considering only the appearance of attributes in the tree. The study is of benefit primarily to researchers, as it compares the impact of attribute weights generated by three different methods and, hence, highlights the fact that the prediction rate of models such as CBR largely depends on the data associated with the parameters used in the model.
  • Article
    Citation - WoS: 87
    Citation - Scopus: 100
    Determining Attribute Weights in a Cbr Model for Early Cost Prediction of Structural Systems
    (American Society of Civil Engineers (ASCE), 2006) Doğan, Sevgi Zeynep; Arditi, David; Günaydın, Hüsnü Murat
    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.
  • Article
    Citation - Scopus: 250
    A Neural Network Approach for Early Cost Estimation of Structural Systems of Buildings
    (Elsevier Ltd., 2004) Günaydın, Hüsnü Murat; Doğan, Sevgi Zeynep
    The importance of decision making in cost estimation for building design processes points to a need for an estimation tool for both designers and project managers. This paper investigates the utility of neural network methodology to overcome cost estimation problems in early phases of building design processes. Cost and design data from thirty projects were used for training and testing our neural network methodology with eight design parameters utilized in estimating the square meter cost of reinforced concrete structural systems of 4-8 storey residential buildings in Turkey, an average cost estimation accuracy of 93% was achieved.