Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Citation - WoS: 29Citation - Scopus: 31Evaluating the Knowledge Management Practices of Construction Firms by Using Importance-Comparative Performance Analysis Maps(American Society of Civil Engineers (ASCE), 2011) Kale, Serdar; Karaman, Erkan A.The emergence of the effective management of knowledge resources as a key factor in gaining and sustaining competitive advantage presents new challenges to construction firms. Evaluating knowledge management practices is considered one of the most important challenges facing firms in today's business environment. This paper proposes a model for evaluating the knowledge management practices of construction firms. The proposed model incorporates knowledge management concepts and multilayer perceptron (MLP) neural networks to construct an importance-comparative performance analysis (ICPA) map, a simple visual tool that can provide powerful diagnostic information to executives of construction firms. The model evaluates a firm's knowledge management practices, identifies its competitive advantages and disadvantages in each knowledge management practice, and sets priorities for managerial actions to improve knowledge management practices. A real-world case study was conducted by administering a survey to 105 construction firms operating in Turkey and is presented to illustrate the implementation and utility of the proposed model. The case study findings provided preliminary support for the validity of the proposed model.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.