Architecture / Mimarlık
Permanent URI for this collectionhttps://hdl.handle.net/11147/24
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Article Citation - WoS: 19Citation - Scopus: 17Problematization of Assessment in the Architectural Design Education: First Year as a Case Study(Elsevier Ltd., 2009) Çıkış, Şeniz; Çil, ElaThis paper discusses the ways in which studio instructors assess students' design and performance during the basic design studios. Architecture requires a discipline-based education in which design studios have primary place in the curriculum. In design studio education the primary focus of assessment has always been the studio production (i.e. end products of the students). There is a common tendency to neglect students' experience and process of learning during assessments. Furthermore, assessment criteria of the studio instructors may not be explicitly stated.Article Citation - WoS: 91Citation - Scopus: 122Artificial Neural Networks To Predict Daylight Illuminance in Office Buildings(Elsevier Ltd., 2009) Kazanasmaz, Zehra Tuğçe; Günaydın, Hüsnü Murat; Binol, SelcenA prediction model was developed to determine daylight illuminance for the office buildings by using artificial neural networks (ANNs). Illuminance data were collected for 3 months by applying a field measuring method. Utilizing weather data from the local weather station and building parameters from the architectural drawings, a three-layer ANN model of feed-forward type (with one output node) was constructed. Two variables for time (date, hour), 5 weather determinants (outdoor temperature, solar radiation, humidity, UV index and UV dose) and 6 building parameters (distance to windows, number of windows, orientation of rooms, floor identification, room dimensions and point identification) were considered as input variables. Illuminance was used as the output variable. In ANN modeling, the data were divided into two groups; the first 80 of these data sets were used for training and the remaining 20 for testing. Microsoft Excel Solver used simplex optimization method for the optimal weights. The model's performance was then measured by using the illuminance percentage error. As the prediction power of the model was almost 98%, predicted data had close matches with the measured data. The prediction results were successful within the sample measurements. The model was then subjected to sensitivity analysis to determine the relationship between the input and output variables. NeuroSolutions Software by NeuroDimensions Inc., was adopted for this application. Researchers and designers will benefit from this model in daylighting performance assessment of buildings by making predictions and comparisons and in the daylighting design process by determining illuminance.Article Citation - Scopus: 250A Neural Network Approach for Early Cost Estimation of Structural Systems of Buildings(Elsevier Ltd., 2004) Günaydın, Hüsnü Murat; Doğan, Sevgi ZeynepThe 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.