Mechanical Engineering / Makina Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/4129
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Article Citation - WoS: 37Citation - Scopus: 41Processing and Characterization of Geopolymer and Sintered Geopolymer Foams of Waste Glass Powders(Elsevier, 2021) Polat, Dilan; Güden, MustafaGeopolymer foams of fine and coarse waste glass (WG) powders were prepared using an activation solution of NaOH (8 M) and Na2SiO3. The effects of WG powder particle size, solid/liquid ratio (S/L = 1, 1.5, and 2) and Al foaming agent content (2-20 wt%) on the expansion and temperature behavior of the slurries were determined in-situ using a laser sensor and a thermocouple, respectively. The geopolymer foams processed using a coarse WG powder slurry, S/L = 2, and 2 wt% Al, were further sintered at 600, 700, 725, and 750 degrees C. The compression strengths and thermal conductivities of the geopolymer and sintered geopolymer foams were also determined. The slurry expansions continued until about a maximum, and the temperatures of the slurries increased to a maximum, 85-88 degrees C. At the maximum temperature, the slurry evaporation and the resultant increase in the S/L ratio limited the slurry expansion. Increasing the Al content decreased the final density of the foams (238-555 kg m-3), while the coarse powder slurries resulted in lower densities than the fine powder slurries. Three crystal phases, muscovite, sodium aluminum silicate hydrate, and thermonitrite, were determined in the geopolymer foams. The muscovite formation was noted to be favored at high S/L ratios. During sintering, the partial melting of glass particles started after about 700 degrees C, while sintering above this temperature decreased the final density of the foams. The reduced density above 700 degrees C was ascribed to the release of CO2 due to the decomposition of thermonitrite. Both the compressive strength and thermal conductivity of the geopolymer and sintered geopolymer foams increased with increasing foam density. The highest increase in the compressive strength and reduction in the density were seen in the geopolymer foams sintered at 750 degrees C.Article Citation - WoS: 6Citation - Scopus: 6The Expansion Behavior of Slurries Containing Recycled Glass Powder Carboxymethyl Cellulose, Lime and Aluminum Powder(Elsevier, 2020) Zeren, Doğuş; Şentürk, Ufuk; Güden, MustafaThe rheology and foaming/expansion of the slurries of a waste/recycled glass powder with 50, 55 and 60 wt% of solid (glass powder) were experimentally investigated. The glass powder slurries were foamed using aluminum powder as foaming agent (0.75 wt%) and calcium hydroxide as activator (1 wt%). Sodium carboxymethyl cellulose (CMC) was added to the slurries as a binder with the amounts between 0 and 4 wt%. The expansions of the slurries were measured in-situ using a laser sensor and reported as percent volume expansion. The CMC-addition increased the viscosities of the slurries, particularly the fine size powder slurries. The slurries with the relatively low-viscosity exhibited lower initial expansion rates compared to the slurries with the relatively high-viscosity. The maximum expansions of the slurries increased from 300 to 350%, when the viscosity increased to 5 Pa s and reached a steady value around 400% between 5 and 50 Pa s. The expansions of the slurries could not be achieved above 50 Pa s since they became too thick to be foamed. The foam samples made from the slurries with 55 and 60 wt% of solid and sintered at 700 and 750 degrees C for 30 min had the average densities between 355 and 530 kg m(-3) and the average compressive strengths between 0.2 and 0.5 MPa. Increasing sintering time to 60 min at 750 degrees C increased the average compressive strength from 0.5 to 1.5 MPa for the foam samples made from the slurry with 60 wt% of solid. These proved that both sintering temperature and time were effective in increasing the compressive strengths of the foamed structures. The thermal conductivities of the sintered foam samples with the densities of 355 and 504 kg m(-3) were measured 0.042 and 0.057 W m(-1) K-1, respectively. (C) 2019 Elsevier Ltd. All rights reserved.Article Citation - WoS: 18Citation - Scopus: 21Evaluation of Heat Treated Clay for Potential Use in Intervention Mortars(Elsevier Ltd., 2010) Budak, Meral; Akkurt, Sedat; Böke, HasanIn this study, raw material compositions, basic physical, mineralogical, microstructural and hydraulic properties of lime mortars used in two selected historic buildings were determined by XRD, SEM-EDS and TGA analyses. The results showed that the mortars were hydraulic due to the use of pozzolanic aggregates. Taking into account the hydraulic characteristics of mortars due to the use of pozzolanic aggregates, the possibility of obtaining hydraulic mortars by using pozzolanic aggregates produced from heated commercial clays was investigated. For this purpose, four clay samples used in the ceramic industry in Turkey were heated at varying temperatures of 400, 450, 500, 550, 600, 800, and 1200°C with a heating rate of 10°C/min. Pozzolanic properties of heated clay samples were determined. The results showed that commercial clays studied are well suited for use as pozzolanic aggregates when they are heated between 500 and 700. °C. This is also confirmed by testing the compressive strengths of the three month aged laboratory-produced mortars that contained thermally treated clay (at 600°C) as pozzolanic aggregates. Compressive strength of this mortar was around 5. MPa which is satisfactorily high. © 2009 Elsevier B.V.Article Citation - WoS: 53Citation - Scopus: 63Artificial Neural Network (ann) Prediction of Compressive Strength of Vartm Processed Polymer Composites(Elsevier Ltd., 2005) Seyhan, Abdullah Tuğrul; Tayfur, Gökmen; Karakurt, Murat; Tanoğlu, MetinA three layer feed forward artificial neural network (ANN) model having three input neurons, one output neuron and two hidden neurons was developed to predict the ply-lay up compressive strength of VARTM processed E-glass/ polyester composites. The composites were manufactured using fabric preforms consolidated with 0, 3 and 6 wt.% of thermoplastic binder. The learning of ANN was accomplished by a backpropagation algorithm. A good agreement between the measured and the predicted values was obtained. Testing of the model was done within low average error levels of 3.28%. Furthermore, the predictions of ANN model were compared with those obtained from a multi-linear regression (MLR) model. It was found that ANN model has better predictions than MLR model for the experimental data. Also, the ANN model was subjected to a sensitivity analysis to obtain its response. As a result, the ANN model was found to have an ability to yield a desired level of ply-lay up compressive strength values for the composites processed with the addition of the thermoplastic binder.Article Citation - WoS: 175Citation - Scopus: 203Fuzzy Logic Model for the Prediction of Cement Compressive Strength(Elsevier Ltd., 2004) Akkurt, Sedat; Tayfur, Gökmen; Can, SeverA fuzzy logic prediction model for the 28-day compressive strength of cement mortar under standard curing conditions was created. Data collected from a cement plant were used in the model construction and testing. The input variables of alkali, Blaine, SO3, and C3S and the output variable of 28-day cement strength were fuzzified by the use of artificial neural networks (ANNs), and triangular membership functions were employed for the fuzzy subsets. The Mamdani fuzzy rules relating the input variables to the output variable were created by the ANN model and were laid out in the If-Then format. Product (prod) inference operator and the centre of gravity (COG; centroid) defuzzification methods were employed. The prediction of 50 sets of the 28-day cement strength data by the developed fuzzy model was quite satisfactory. The average percentage error levels in the fuzzy model were successfully low (2.69%). The model was compared with the ANN model for its error levels and ease of application. The results indicated that through the application of fuzzy logic algorithm, a more user friendly and more explicit model than the ANNs could be produced within successfully low error margins.Article Citation - WoS: 135Citation - Scopus: 157The Use of Ga-Anns in the Modelling of Compressive Strength of Cement Mortar(Elsevier Ltd., 2003) Akkurt, Sedat; Özdemir, Serhan; Tayfur, Gökmen; Akyol, BurakIn this paper, results of a project aimed at modelling the compressive strength of cement mortar under standard curing conditions are reported. Plant data were collected for 6 months for the chemical and physical properties of the cement that were used in model construction and testing. The training and testing data were separated from the complete original data set by the use of genetic algorithms (GAs). A GA-artificial neural network (ANN) model based on the training data of the cement strength was created. Testing of the model was also done within low average error levels (2.24%). The model was subjected to sensitivity analysis to predict the response of the system to different values of the factors affecting the strength. The plots obtained after sensitivity analysis indicated that increasing the amount of C3S, SO3 and surface area led to increased strength within the limits of the model. C2S decreased the strength whereas C3A decreased or increased the strength depending on the SO3 level. Because of the limited data range used for training, the prediction results were good only within the same range. The utility of the model is in the potential ability to control processing parameters to yield the desired strength levels and in providing information regarding the most favourable experimental conditions to obtain maximum compressive strength.