Yazar "Bozkir, Samet Mufit" seçeneğine göre listele

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    AN EXPERIMENTAL EVALUATION OF WORKABILITY AND BLEEDING BEHAVIORS OF ULTRA-SONICATED NANO ZINC OXIDE (n-ZnO) DOPED CEMENT PASTES INCORPORATED WITH FLY ASH
    (Begell House Inc, 2024) Celik, Fatih; Colak, Andac Batur; Yildiz, Oguzhan; Bozkir, Samet Mufit
    In this study, the workability and bleeding behaviors of ultra-sonicated nano zinc oxide (n-ZnO) doped cement pastes incorporated with fly ash have been experimentally investigated. Therefore, the effects of nano zinc oxide (n-ZnO) additions at different amounts by mass (0.0, 0.3, 0.6, 0.9, 1.2, and 1.5%) on the bleeding and the workability properties (mars cone flow time, mini slump spread diameter, and plate cohesion) of cement -based grouts incorporated with fly ash (FA) as mineral additive at different constitutes (0% -for control purpose, 5, 10, 15, 20, 25, and 30%) were investigated. The use of FA as a mineral additive in grout samples resulted in improvements in the workability behavior of the grout samples as expected. Increase amount of n-ZnO in the grout mixtures has made mini slump flow diameter of the samples noticeably decrease. Although certain changes seem to have been observed, it has been understood that the increase in the amount of n-ZnO in the injection matrix generally does not change the Marsh cone flow time of mineral -added cement -based grouts. Remarkable increases in plate cohesion values were measured because of the increase in the content of nano zinc oxide for all mixtures. At the same time, just like the FA effect, bleeding values tend to decrease due to the increase in the amount of nano zinc oxide in grout mixes. Moreover, the results obtained showed that the artificial neural network model can make predictions with very high accuracy.
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    An Experimental Investigation on Workability and Bleeding Behaviors of Cement Pastes Doped with Nano Titanium Oxide (n-TiO2) Nanoparticles and Fly Ash
    (Tech Science Press, 2023) Celik, Fatih; Yildiz, Oguzhan; Colak, Andac Batur; Bozkir, Samet Mufit
    In this study, the workability of cement-based grouts containing n-TiO2 nanoparticles and fly ash has been investigated experimentally. Several characteristic quantities (including, but not limited to, the marsh cone flow time, the mini slump spreading diameter and the plate cohesion meter value) have been measured for different percentages of these additives. The use of fly ash as a mineral additive has been found to result in improvements in terms of workability behavior as expected. Moreover, if nano titanium oxide is also used, an improvement can be obtained regarding the bleeding values for the cement-based grout mixes. Using such experimental data, a multi-layer perceptron artificial neural network model has been developed (5 neurons in the hidden layer of the network model have been developed using a total of 42 experimental data). 70% of the data employed in this model have been used for training, 15% for validation and 15% for the test phase. The results demonstrate that the artificial neural network model can predict Marsh cone flow time, mini slump spreading diameter and plate cohesion meter values with an average error of 0.15%.
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    An Experimental Investigation on Workability and Bleeding Features
    (Amer Concrete Inst, 2022) Celik, Fatih; Colak, Andac Batur; Yildiz, Oguzhan; Bozkir, Samet Mufit
    In this experimental study, the workability and bleeding properties of cement-based grout mixtures combined with fly ash (FA) and colloidal nanopowder (n-Al2O3) were investigated, and some prediction models were developed with an artificial neural network (ANN). Marsh cone flow time, mini-slump spreading diameter, and Lombardi plate cohesion of the grout samples were measured based on the workability test. Test results showed that the use of FA as mineral additive in the grout samples positively contributed to an increase of the fluidity of the grout samples as expected. Considerable effects were observed on workability features of grout mixtures with the addition of nano alumina because of having a large specific surface area. In addition, the use of nano alumina together with FA in grout mixtures contributes to the stability of these mixtures by looking at changes in bleeding values. Using the experimental data obtained, an ANN model was developed to predict the values of Marsh cone flow time, mini-slump spreading diameter, and plate cohesion. The developed ANN model can predict mini-slump spreading diameter with an error rate of -0.04%, Marsh cone flow time value with an error rate of -0.23%, and plate cohesion value with an error rate of -1.07%.
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    Analysing of nano-silica usage with fly ash for grouts with artificial neural network models
    (Emerald Group Publishing Ltd, 2023) Celik, Fatih; Yildiz, Oguzhan; Colak, Andac Batur; Bozkir, Samet Mufit
    When grout is used to penetrate voids and cracks in soils and rock layers, easy and effective pumping of the grouts is vital, especially for grouting works during geotechnical improvements. For this reason, improving the rheological parameters of cement-based grouts and increasing their fluidity are important for effective grouting injection. In this study, an experimental investigation and analysis using artificial neural network (ANN) models were used to discover how nano silica (n-SiO2) together with fly ash affects the rheological behaviour of cement-based grouts. The effects of nano silica (n-SiO2) additions at different contents by mass (0.0%, 0.3%, 0.6%, 0.9%, 1.2% and 1.5%) on the plastic viscosity and yield stress values of cement-based grouts incorporating fly ash as a mineral additive at different amounts (0% - as a control, 5%, 10%, 15%, 20%, 25% and 30%) were investigated. Using the experimental data obtained, a feed-forward (FF) back-propagation (BP) multi-layer perceptron (MLP) artificial neural network (ANN) was developed to predict the plastic viscosity and yield stress of cement-based grouts with nano silica nanoparticle additives. The ANN model developed can predict the plastic viscosity and yield stress values of cement-based grouts containing nano silica nanoparticle-doped fly ash with high accuracy.
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    Developing Prediction Model on Workability Parameters of Ultrasonicated Nano Silica (n- SiO2) and Fly Ash Added Cement-Based Grouts by Using Artificial Neural Networks
    (Amer Soc Testing Materials, 2022) Colak, Andac Batur; Yildiz, Oguzhan; Celik, Fatih; Bozkir, Samet Mufit
    In this experimental study, the workability and bleeding properties of cement-based grout mixes combined with fly ash (FA) and nano silica (n-SiO2) as colloidal nanopowder were investigated, and some prediction models were developed with the artificial neural network. The Marsh cone flow time, mini slump spreading diameter, and plate cohesion meter values of samples prepared in different concentrations have been measured and analyzed experimentally to investigate the workability properties. Moreover, bleeding tests were carried out on the grout mixtures prepared within the scope of this experimental study. Test results showed that the usage of FA as a mineral additive in the grout samples positively contributed to an increase on the fluidity of the grout samples as expected. Although the increase in n-SiO2 content in the grout mixes resulted in an increase in the Marsh cone flow time of the grout mixes, it resulted in a decrease in the mini slump spreading diameter of the samples. The increase in the plate cohesion values of the grout mixtures was also observed in the n-SiO2 added grout mixtures. At the same time, the bleeding values of the grout mixes with and without mineral additives of 0.9 % or more with n-SiO2 additives remained above 900 ml (below 10 % bleeding rate). The artificial neural network model can predict the workability properties of cement-based grouts containing n-SiO2 nanoparticle-doped FA with high accuracy.