Mechanical Engineering / Makina Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/4129

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Author: search.filters.author.Akkurt, SedatSubject: search.filters.subject.Compressive strength

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Now showing 1 - 3 of 3
  • Article
    Citation - WoS: 18
    Citation - Scopus: 21
    Evaluation of Heat Treated Clay for Potential Use in Intervention Mortars
    (Elsevier Ltd., 2010) Budak, Meral; Akkurt, Sedat; Böke, Hasan
    In 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: 175
    Citation - Scopus: 203
    Fuzzy Logic Model for the Prediction of Cement Compressive Strength
    (Elsevier Ltd., 2004) Akkurt, Sedat; Tayfur, Gökmen; Can, Sever
    A 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: 135
    Citation - Scopus: 157
    The Use of Ga-Anns in the Modelling of Compressive Strength of Cement Mortar
    (Elsevier Ltd., 2003) Akkurt, Sedat; Özdemir, Serhan; Tayfur, Gökmen; Akyol, Burak
    In 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.