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All Journal International Journal of Intelligent Systems and Applications in Engineering
ALTIN, Mustafa
Advanced Technology and Science (ATScience)
Computer Science & IT

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Estimating of Compressive Strength of Concrete with Artificial Neural Network According to Concrete Mixture Ratio and Age Ozkan, Ilker Ali; ALTIN, Mustafa
International Journal of Intelligent Systems and Applications in Engineering Vol 4, No 3 (2016)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.263977

Abstract

Compressive strength of concrete is one of the most important elements for an existing building and a new structure to be built. While obtaining the desired compressive strength of concrete with an appropriate mix and curing conditions for a new structure, with non-destructive testing methods for an existing structure or by taking core samples the concrete compressive strength are determined. One of the most important factors that affects the concrete compressive strength is age of concrete. In this study, it is attempted to estimate compressive strength, modelling Artificial Neural Networks (ANN) and using different mixture ratios and compressive strength of concrete samples at different ages. In accordance with obtained data’s in the estimation of concrete compressive strength, ANN could be used safely.
Application of ANN Modelling of Fire Door Resistance Altin, Mustafa; Tasdemir, Sakir
International Journal of Intelligent Systems and Applications in Engineering Vol 4, No 2 (2016)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.90445

Abstract

Fire doors are compulsorily used in every kind of building nowadays. The determination of fire doors’ resistance in which kind of buildings is also essential. This determination is needed to be watched through the experimental works done. Computer technologies and applications are commonly used in many fields in industry. In this study, by using the data obtained as a result of experiments made in order to determine the resistance of fire doors, artificial neural network (ANN) model was developed. With this model, it is aimed to evaluate the inner temperature of fire room having an important role in resistance of the fire door. In the developed system, temperature values belonging to thermocouples on the door (Top Left, Top  Right, Middle Left, Middle Right, Bottom Left, Bottom Right (oC) and time (minute) were taken as input parameters and in-room temperature (oC) was taken as output parameters. When the results obtained from ANN and experimental data are compared, it is determined that two groups of data were coherent. It is shown that ANN can be safely used in the determination of fire door resistance. 
Co-Authors Ozkan, Ilker Ali TASDEMIR, Sakir