Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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

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Subject: search.filters.subject.Artificial neural networkWoS Q: search.filters.wosQuartile.Q2

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  • Article
    Citation - Scopus: 1
    Relationship Between Abrasion, Fragmentation and Thermal Weathering Resistance of Aggregates: Regression and Artificial Neural Network Analyses
    (Springer, 2023) Gökalp, İslam; Kaya, Orhan; Uz, Volkan Emre
    For being used in pavement construction, properties of aggregates must satisfy the minimum requirements specified by highway agencies or institutions. The properties of the aggregates are determined by many tests lasting anywhere between a couple of hours to a few weeks depending on the type of the test. If good correlations can be established between the tests taking longer time and the ones taking comparably shorter time, there might be no need to conduct these longer time-taking tests for the sake of time. The aim of this study is to investigate the relationships between abrasion, fragmentation, and thermal weathering resistances of different aggregate types. To accomplish this aim, aggregates with different origins (natural and slags) were tested and correlative analyses utilizing regression analysis and artificial neural network (ANN) models were performed to establish relationships between the results of these test methods. It was found that good correlations can be established especially with ANN models and significant amount of time and effort can be saved with these developed models. © 2023, The Author(s), under exclusive licence to Chinese Society of Pavement Engineering.
  • Article
    Citation - WoS: 40
    Citation - Scopus: 53
    Models for Prediction of Daily Mean Indoor Temperature and Relative Humidity: Education Building in Izmir, Turkey
    (SAGE Publications Inc., 2012) Özbalta, Türkan Göksal; Sezer, Alper; Yıldız, Yusuf
    In this research, several models were developed to forecast the daily mean indoor temperature (IT) and relative humidity values in an education building in Izmir, Turkey. The city is located at a hot-humid climatic region. In order to forecast the IT and internal relative humidity (IRH) parameters in the building, a number of artificial neural networks (ANN) models were trained and tested with a dataset including outdoor climatic conditions, day of year and indoor thermal comfort parameters. The indoor thermal comfort parameters, namely, IT and IRH values between 6 June and 21 September 2009 were collected via HOBO data logger. Fraction of variance (R2) and root-mean squared error values calculated by the use of the outputs of different ANN architectures were compared. Moreover, several multiple regression models were developed to question their performance in comparison with those of ANNs. The results showed that an ANN model trained with inconsiderable amount of data was successful in the prediction of IT and IRH parameters in education buildings. It should be emphasized that this model can be benefited in the prediction of indoor thermal comfort conditions, energy requirements, and heating, ventilating and air conditioning system size. © The Author(s), 2011. Reprints and permissions: