Browsing by Author "Demir I."

Now showing 1 - 4 of 4

Effects of the fine recycled concrete aggregates on the concrete properties
(2011-05-18) Yaprak H.; Aruntas H.; Demir I.; Simsek O.; Durmus G.
In this experimental study, the effects of the recycled fine recycled concrete aggregate (FRA) that was manufactured from concrete wastes on the concrete properties were investigated. In concrete mixtures, 0, 10, 20, 30, 40, 50 and 100% by weight FRA were used instead of river sand. Afterwards, unit weight and water absorption ratios and 28-day compressive strength were determined. According to the test results obtained, it was seen that FRA can be used up to 10% ratio for producing C30 concrete, between 20-50% ratios for producing C25 concrete. Thus, environmental impacts and consumption of the natural resources can be significantly reduced by using recycled fine concrete aggregates in concrete applications. © 2011 Academic Journals.
Estimating the properties of ground-waste-brick mortars using DNN and ANN
(2019-01-01) Karaci A.; Yaprak H.; Ozkaraca O.; Demir I.; Simsek O.
In this study, deep-neural-network (DNN)- and artificial-neural-network (ANN)-based models along with regression models have been developed to estimate the pressure, bending and elongation values of ground-brick (GB)-added mortar samples. This study is aimed at utilizing GB as a mineral additive in concrete in the ratios 0.0%, 2.5%, 5.0%, 7.5%, 10.0%, 12.5% and 15.0%. In this study, 756 mortar samples were produced for 84 different series and were cured in tap water (W), 5% sodium sulphate solution (SS5) and 5% ammonium nitrate solution (AN5) for 7 days, 28 days, 90 days and 180 days. The developed DNN models have three inputs and two hidden layers with 20 neurons and one output, whereas the ANN models have three inputs, one output and one hidden layer with 15 neurons. Twenty-five previously obtained experimental sample datasets were used to train these developed models and to generate the regression equation. Fifty-nine non-training-attributed datasets were used to test the models. When these test values were attributed to the trained DNN, ANN and regression models, the brick-dust pressure as well as the bending and elongation values have been observed to be very close to the experimental values. Although only a small fraction (30%) of the experimental data were used for training, both the models performed the estimation process at a level that was in accordance with the opinions of experts. The fact that this success has been achieved using very little training data shows that the models have been appropriately designed. In addition, the DNN models exhibited better performance as compared with that exhibited by the ANN models. The regression model is a model whose performance is worst and unacceptable; further, the prediction error is observed to be considerably high. In conclusion, ANN- and DNN-based models are practical and effective to estimate these values.
Performance of cement mortars replaced by ground waste brick in different aggressive conditions
(2011-11-03) Demir I.; Yaprak H.; Simsek O.
This article investigates the sulphate resistance of cement mortars when subjected to different exposure conditions. Cement mortars were prepared using ground waste brick (GWB) as a pozzolanic partial replacement for cement at replacement levels of 0%, 2.5%, 5%, 7.5, 10%, 12.5 and 15%. Mortar specimens were stored under three different conditions: continuous curing in lime-saturated tab water (TW), continuous exposure to 5% sodium sulphate solution (SS), and continuous exposure to 5% ammonium nitrate solution (AN), at a temperature of 20 ± 3°C, for 7, 28, 90, and 180 days. Prisms with dimensions of 25×25×285 mm, to determine the expansions of the mortar samples; and another set of prisms with dimensions of 40×40×160 mm, were prepared to calculate the compressive strength of the samples. It was determined that the GWB replacement ratios between 2.5% and 10% decreased the 180 days expansion values. The highest compressive strength values were found for the samples with 10% replacement ratio in the TW, SS, and AN conditions for 180 days. The microstructure of the mortars were investigated using scanning electron microscopy (SEM) and the Energy dispersive X-ray (EDX).
Prediction of the effect of varying cure conditions and w/c ratio on the compressive strength of concrete using artificial neural networks
(2013-01-01) Yaprak H.; Karaci A.; Demir I.
The present study aims at developing an artificial neural network (ANN) to predict the compressive strength of concrete. A data set containing a total of 72 concrete samples was used in the study. The following constituted the concrete mixture parameters: two distinct w/c ratios (0.63 and 0.70), three different types of cements and three different cure conditions. Measurement of compressive strengths was performed at 3, 7, 28 and 90 days. Two different ANN models were developed, one with 4 input and 1 output layers, 9 neurons and 1 hidden layer, and the other with 5, 6 neurons, 2 hidden layers. For the training of the developed models, 60 experimental data sets obtained prior to the process were used. The 12 experimental data not used in the training stage were utilized to test ANN models. The researchers have reached the conclusion that ANN provides a good alternative to the existing compressive strength prediction methods, where different cements, ages and cure conditions were used as input parameters. © 2011 Springer-Verlag London Limited.