Civil Engineering / İnşaat Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/13

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Department: search.filters.department.İzmir Institute of Technology. Mechanical EngineeringPublisher: search.filters.publisher.Elsevier Ltd.

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Now showing 1 - 6 of 6
  • Article
    Citation - WoS: 97
    Citation - Scopus: 110
    Effect of Polyamide-6,6 (pa 66) Nonwoven Veils on the Mechanical Performance of Carbon Fiber/Epoxy Composites
    (Elsevier Ltd., 2018) Beylergil, Bertan; Tanoğlu, Metin; Aktaş, Engin
    In this study, carbon fiber/epoxy (CF/EP) composites were interleaved with polyamide-6,6 (PA 66) nonwoven veils at two different areal weight densities (17 and 50 gsm) to improve their delamination resistance against Mode-I loading. Mode-I fracture toughness (DCB), tensile, open hole tensile (OHT), flexural, compression, short beam shear (ILSS) and Charpy-impact tests were performed on the reference and PA 66 interleaved composite specimens. The DCB test results showed that the initiation and propagation Mode-I fracture toughness values of the composites were significantly improved by 84 and 171% using PA 66-17 gsm veils respectively, as compared to reference laminates. The use of denser PA 66-50 gsm veils in the interlaminar region led to higher improvement in fracture toughness values (349% for initiation and 718% for propagation) due to the higher amount of veil fibers involved in fiber bridging toughening mechanism. The incorporation of PA 66-50 gsm nonwoven veils also increased the ILSS and Charpy impact strength of the composites by 25 and 15%, respectively. On the other hand, the PA 66 veils reduced in-plane mechanical properties of CF/EP composites due to lower carbon fiber volume fraction and increased thickness.
  • Article
    Citation - WoS: 2
    Citation - Scopus: 2
    Trait-based heterogeneous populations plus (TbHP+) genetic algorithm
    (Elsevier Ltd., 2009) Tayfur, Gökmen; Sevil, Hakkı Erhan; Gezgin, Erkin; Özdemir, Serhan
    This study developed a variant of genetic algorithm (GA) model called the trait-based heterogeneous populations plus (TbHP+). The developed TbHP+ model employs a memory concept in the form of immunity and instinct to provide the populations with a more efficient guidance. Also, it has an ability to vary the number of individuals during the search process, thus allowing an automatic determination of the size of the population based on the individual qualities such as character fitness and credit for immunity. The algorithm was tested against the classical GA model in convergence and minimum error performance. For this purpose, 5 different mathematical functions from the literature were employed. The selected functions have different topological characteristics, ranging from simple convex curves with 2 variables to complex trigonometric ones having several hilly shapes with more than 2 variables. The developed model and the classical GA model were applied to finding the global minima of the functions. The comparison of the results revealed that the developed TbHP+ model outperformed the classical GA in faster convergence and minimum errors, which may be explained by the adaptive nature of the new paradigm.
  • Article
    Citation - WoS: 53
    Citation - Scopus: 63
    Artificial Neural Network (ann) Prediction of Compressive Strength of Vartm Processed Polymer Composites
    (Elsevier Ltd., 2005) Seyhan, Abdullah Tuğrul; Tayfur, Gökmen; Karakurt, Murat; Tanoğlu, Metin
    A 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: 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.
  • Article
    Citation - WoS: 68
    Citation - Scopus: 86
    Fuzzy Logic Algorithm for Runoff-Induced Sediment Transport From Bare Soil Surfaces
    (Elsevier Ltd., 2003) Tayfur, Gökmen; Özdemir, Serhan; Singh, Vijay P.
    Utilizing the rainfall intensity, and slope data, a fuzzy logic algorithm was developed to estimate sediment loads from bare soil surfaces. Considering slope and rainfall as input variables, the variables were fuzzified into fuzzy subsets. The fuzzy subsets of the variables were considered to have triangular membership functions. The relations among rainfall intensity, slope, and sediment transport were represented by a set of fuzzy rules. The fuzzy rules relating input variables to the output variable of sediment discharge were laid out in the IF-THEN format. The commonly used weighted average method was employed for the defuzzification procedure. The sediment load predicted by the fuzzy model was in satisfactory agreement with the measured sediment load data. Predicting the mean sediment loads from experimental runs, the performance of the fuzzy model was compared with that of the artificial neural networks (ANNs) and the physics-based models. The results of showed revealed that the fuzzy model performed better under very high rainfall intensities over different slopes and over very steep slopes under different rainfall intensities. This is closely related to the selection of the shape and frequency of the fuzzy membership functions in the fuzzy model.