Civil Engineering / İnşaat Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/13
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Article Citation - WoS: 8Citation - Scopus: 9Dynamic Behavior Predictions of Fiber-Metal Laminate/Aluminum Foam Sandwiches Under Various Explosive Weights(SAGE Publications, 2016) Baştürk, Suat Bahar; Tanoğlu, Metin; Çankaya, Mehmet Alper; Eğilmez, Oğuz ÖzgürApplication of blast tests causes some problems to characterize the performance of panels due to the drastic conditions of explosive medium. Real test has high safety concerns and is not easily accessible because of its extra budget. Some approaches are needed for the preliminary predictions of dynamic characteristics of panels under blast loading conditions. In this study, the response of sandwiches under blast effect was evaluated by combining quasi-static experiments and computational blast test data. The primary aim is to relate the quasi-static panel analysis to dynamic blast load. Based on this idea, lightweight sandwich composites were subjected to quasi-static compression loading with a special test apparatus and the samples were assumed as single degree-of-freedom mass-spring systems to include dynamic effect. This approach provides a simpler way to simulate the blast loading over the surface of the panels and reveals the possible failure mechanisms without applying any explosives. Therefore the design of the panels can be revised by considering quasi-static test results. In this work, the peak deflections and survivabilities of sandwiches for various explosive weights were predicted based on the formulations reported in the literature. Major failure types were also identified and evaluated with respect to their thicknesses.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.