Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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  • Conference Object
    Citation - Scopus: 1
    Observed Performance of a RC Wall-Frame Building During the February 2023 Turkey Earthquake and Performance Improvement Using FRPs
    (International Institute for FRP in Construction (IIFC), 2023) Tura, C.; Sahinkaya, Y.; Güllü, M.F.; Demir, U.; Orakcal, K.; Ilki, A.
    In this study, results of nonlinear response history analysis are presented for an existing RC wall-frame building, which has suffered collapse-level damage during the devastating February 2023 Kahramanmaras earthquakes. Performance analysis results for two building configurations are compared; first for the existing building configuration generated upon on-site observations, and second, for a hypothetical configuration in which the structural walls and columns are retroffited using externally-bonded FRP sheets. Analysis results reveal that in its existing configuration, mostly due to detailing deficiencies, a collapse-level performance was not unexpected; whereas FRP strengthening of the building would have resulted in collapse-prevention performance. © CICE 2023 - 11th International Conference on FRP Composites in Civil Engineering. All rights reserved.
  • 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.
  • Article
    Citation - WoS: 63
    Citation - Scopus: 77
    Artificial Neural Networks Applications in Building Energy Predictions and a Case Study for Tropical Climates
    (John Wiley and Sons Inc., 2005) Yalçıntaş, Melek; Akkurt, Sedat
    This study presents artificial neural network (ANN) methods in building energy use predictions. Applications of the ANN methods in energy audits and energy savings predictions due to building retrofits are emphasized. A generalized ANN model that can be applied to any building type with minor modifications would be a very useful tool for building engineers. ANN methods offer faster learning time, simplicity in analysis and adaptability to seasonal climate variations and changes in the building's energy use when compared to other statistical and simulation models. The model herein is presented for predicting chiller plant energy use in tropical climates with small seasonal and daily variations. It was successfully created based on both climatic and chiller data. The average absolute training error for the model was 9.7% while the testing error was 10.0%. This indicates that the model can successfully predict the particular chiller energy consumption in a tropical climate.