Architecture / Mimarlık

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

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End date: 2009Institution Author: search.filters.institutionAuthor.Kazanasmaz, Zehra Tuğçe

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  • Article
    Citation - WoS: 3
    Citation - Scopus: 3
    The Impact of Planimetric Configuration on Structurally Damaged Residential Buildings
    (Taylor and Francis Ltd., 2009) Kazanasmaz, Zehra Tuğçe
    This study was conducted to determine a significant relationship between planimetric configuration and vulnerability of hazardous buildings located in seismic zones by developing design and construction efficiency indicators. Case study examples were chosen from residential buildings in Bolu, Düzce and Kaynasli in Turkey, which were damaged by the 1999 earthquakes. Utilizing field survey drawings, efficiency quotients; compactness quotients; construction efficiency ratios; aspect ratios and height-to-base ratios were defined as planimetric configuration indicators. The significant relationship between these aspects and the damage level of buildings were determined through statistical analyses and scatter charts. Planimetric configuration - including building geometry, cantilever projections and layout of columns -was reviewed according to the Turkish Earthquake Code. Findings revealed certain dependencies for efficiency ratios, which would satisfactorily predict the seismic vulnerability of buildings based on planimetric configuration. Researchers in the field of architecture who are engaged in earthquake-resistant design may use the general methodology. In addition, architects and structural engineers can use this approach presented here to evaluate their design.
  • Article
    Citation - WoS: 91
    Citation - Scopus: 122
    Artificial Neural Networks To Predict Daylight Illuminance in Office Buildings
    (Elsevier Ltd., 2009) Kazanasmaz, Zehra Tuğçe; Günaydın, Hüsnü Murat; Binol, Selcen
    A 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.