Yazar "Ceylan, M." seçeneğine göre listele

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    AN ANN APPROACHES ON ESTIMATING EARTHQUAKE PERFORMANCES OF EXISTING RC BUILDINGS
    (ACAD SCIENCES CZECH REPUBLIC, INST COMPUTER SCIENCE, 2012) Arslan, M. H.; Ceylan, M.; Koyuncu, T.
    This study aims at developing an artificial intelligence-based (ANN based) analytical method to analyze earthquake performances of the reinforced concrete (RC) buildings. In the scope of the present study, 66 real RC buildings with four to ten storeys were subject to performance analysis according to 19 parameters considered effective on the performance of R,C buildings. In addition, the level of performance of these buildings in case of an earthquake was determined on the basis of the 4-grade performance levels specified in Turkish Earthquake Code-2007 (TEC-2007). Thus, an output performance data group was created for the analyzed buildings, in accordance with the input data. Thanks to the ANN-based fast evaluation algorithm mentioned above and developed within the scope of the proposed project study, it will be possible to make an economic and rapid evaluation of four to ten-storey RC buildings in Turkey with great accuracy (about 80%). Detection of post-earthquake performances of RC buildings in the scope of the present study will facilitate reaching important results in terms of buildings, which will be beneficial for Civil Engineers of Turkey and similar countries.
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    A New Application Area of ANN and ANFIS: Determination of Earthquake Load Reduction Factor of Prefabricated Industrial Buildings
    (Taylor & Francis Ltd, 2010) Ceylan, M.; Arslan, M. H.; Ceylan, R.; Kaltakcı, M. Y.; Özbay, Y.
    The earthquake load reduction factor, R, is one of the most important parameters in the design stage of a building. Significant damages and failures were experienced on prefabricated reinforced concrete structures during the last earthquakes in Turkey and the experts agreed that they resulted mainly from the incorrectly selected earthquake load reduction factor, R. In this study, an attempt was made to estimate the R coefficient for prefabricated industrial structures having a single storey, one and two bays, which are commonly constructed for manufacturing andwarehouse operation with variable dimensions. According to the selected variable dimensions, 280 sample (140 samples for one bay (S-1) and 140 samples for two bays (S-2)) frames' load-displacement relations were computed using pushover analysis and the earthquake load reduction factor, R, was calculated for each frame. Then, formulated three-layered artificial neural network methods (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) were trained by using 214 of the 280 sample frames. Then, the methods were tested with the other 66 sample frames. Accuracy rates were found to be about 94% and 96% for ANN and ANFIS, respectively. The use of ANN and ANFIS provided an alternative way for estimating the R and it also showed that ANFIS estimated R more successfully than ANN.
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    STATISTICAL FEATURE EXTRACTION BASED ON AN ANN APPROACH FOR ESTIMATING THE COMPRESSIVE STRENGTH OF CONCRETE
    (ACAD SCIENCES CZECH REPUBLIC, INST COMPUTER SCIENCE, 2015) Dogan, G.; Arslan, M. H.; Ceylan, M.
    Applications of artificial intelligence in engineering disciplines have become widespread and have provided alternative solutions to engineering problems. Image processing technology (IPT) and artificial neural networks (ANNs) are types of artificial intelligence methods. However, IPT and ANN have been used together in extremely few studies. In this study, these two methods were used to determine the compressive strength of concrete, a complex material whose mechanical features are difficult to predict. Sixty cube-shaped specimens were manufactured, and images of specific features of the specimens were taken before they were tested to determine their compressive strengths. An ANN model was constituted as a result of the process of digitizing the images. In this way, the two different artificial intelligence methods were used together to carry out the analysis. The compressive strength values of the concrete obtained via analytical modeling were compared with the test results. The results of the comparison (R-2 = 0.9837-0.9961) indicate that the combination of these two artificial intelligence methods is highly capable of predicting the compressive strengths of the specimens. The model's predictive capability was also evaluated in terms of several statistical parameters using a set of statistical methods during the digitization of the images constituting the artificial neural network.