Architecture / Mimarlık

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

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Institution Author: search.filters.institutionAuthor.Doğan, Sevgi ZeynepPublisher: search.filters.publisher.American Society of Civil Engineers (ASCE)

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Now showing 1 - 3 of 3
  • Article
    Citation - WoS: 65
    Citation - Scopus: 81
    Assessing Coordination Performance Based on Centrality in an E-Mail Communication Network
    (American Society of Civil Engineers (ASCE), 2015) Doğan, Sevgi Zeynep; Arditi, David; Günhan, Suat; Erbaşaranoğlu, Bengi
    Building design and construction require the collective effort of diverse project participants. The coordination performance of these project participants is important for effective management and needs to be assessed periodically. However, there is no uncomplicated quantitative way to measure coordination. Measuring coordination is cumbersome and time-consuming particularly during the project execution phase. This study proposes an easy procedure for monitoring the coordinative performance of project participants. The degree, betweenness, and closeness centrality measures of the project participants in a wayfinding signage project at a major airport construction project are calculated using social network analysis on the e-mail communication network between the participants. A centrality index is defined for each firm based on the average of these three centrality measures. The firm's coordination score is also calculated based on content analysis of the sent and received e-mails between the participants. The coordination scores are found to be highly correlated with the centrality indices. To define the coordinative role of a firm, its centrality index could therefore be measured easily using a simple software and only the number and direction of e-mails exchanged between
  • 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.