Mechanical Engineering / Makina Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/4129
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Article Constitutive Equation Determination and Dynamic Numerical Modelling of the Compression Deformation of Concrete(Wiley, 2021) Seven, Semih Berk; Çankaya, M. Alper; Uysal, Çetin; Taşdemirci, Alper; Saatci, Selçuk; Güden, MustafaThe dynamic compression deformation of an in-house cast concrete (average aggregate size of 2-2.5 mm) was modelled using the finite element (FE), element-free Galerkin (EFG) and smooth particle Galerkin (SPG) methods to determine their capabilities of capturing the dynamic deformation. The numerical results were validated with those of the experimental split Hopkinson pressure bar tests. Both EFG and FE methods overestimated the failure stress and strain values, while the SPG method underestimated the peak stress. SPG showed similar load capacity profile with the experiment. At initial stages of the loading, all methods present similar behaviour. Nonetheless, as the loading continues, the SPG method predicts closer agreement of deformation profile and force histories. The increase in strength at high strain rate was due to both the rate sensitivity and lateral inertia caused by the confinement effect. The inertia effect of the material especially is effective at lower strain values and the strain rate sensitivity of the concrete becomes significant at higher strain values.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.