Yazar "Kurtoğlu, Ahmet Emin" seçeneğine göre listele
Listeleniyor 1 - 5 / 5
Sayfa Başına Sonuç
Sıralama seçenekleri
Öğe Development of fly ash/slag based self-compacting geopolymer concrete using nano-silica and steel fiber(ELSEVIER SCI LTD, THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND, 2019) Gülşan, Mehmet Eren; Alzeebaree, Radhwan; Rasheed, Ayad Ali; Niş, Anıl; Kurtoğlu, Ahmet EminThis study investigates the simultaneous effect of nano-silica and steel fiber on the fresh and hardened state performance of self-compacting geopolymer concretes (SCGC). For this purpose, self-compacting geopolymer concretes without and with nano-silica (0, 1% and 2%), and without and with steel fiber (0, 0.5% and 1%) were produced. Hooked-end steel fibers were used with a length of 30 mm and an aspect ratio of 40. Self-compacting geopolymer mixes were produced using 50% fly ash (FA) and 50% ground granulated blast furnace slag (GGBFS) with a constant alkaline activator to binder ratio of 0.5. For the alkaline activator, sodium silicate solution (Na2SiO3) and sodium hydroxide solution (NaOH) were utilized with a ratio (Na2SiO3/NaOH) of 2.5. Fresh state experiments were carried out via slump flow, L-Box, and V-funnel tests, while hardened state experiments were conducted using compressive strength, flexural strength, and bonding strength tests to estimate the effects of nano-silica and steel fiber together on the resulting performances of SCGC specimens. Test results were also evaluated statistically in order to clarify the contributions of the important parameters on the resulting performance. Moreover, correlations between the experimental data were studied to investigate the relationships between the fresh and hardened state performances. The results demonstrated that incorporation of nano-silica and steel fiber affected the fresh state properties adversely; however, a combined utilization of them improved bond strength and flexural performance of the SCGC specimens significantly. In addition, the effect of nano-silica was found to be dominant on fresh state properties and compressive strength, while the effect of steel fiber was found to be superior on flexural performance and bonding strength.Öğe Modeling the Shear Strength of Reinforced Aerated Concrete Slabs via Support Vector Regression(İstanbul Gelişim Üniversitesi Yayınları / Istanbul Gelisim University Press, 2019-03-29) Kurtoğlu, Ahmet Emin; Bakbak, DeryaAbstract- Autoclaved aerated concrete (AAC) attracts attention as it provides superior material characteristics such as high thermal insulation and environmentally friendly properties. Apart from non-structural applications, AAC is being considered as a structural material thanks to its characteristics such as lighter weight compared to normal concrete, resulting in lower design costs. This study focuses on the feasibility of support vector regression (SVR) in predicting the shear resistance of reinforced AAC slabs. An experimental dataset with 271 data points extracted from eleven sources is used to develop models. Based on random selection, the dataset is divided into two portions, 75% for model development and 25% for testing the validity of the model. Two SVR model types (epsilon and Nu) and four kernel functions (linear, polynomial, sigmoid and radial basis) are used for model development and the results of each model and kernel type is presented in terms of correlation coefficient (R 2 ) and mean squared error (MSE). Results show that epsilon model type with radial basis function yields the best SVR model. Keywords Autoclaved aerated concrete, reinforced concrete slab, shear strength, support vector regression, modelling.Öğe New Model for Compressive Strength Loss of Lightweight Concrete Exposed to Elevated Temperatures(VINCA INST NUCLEAR SCI, MIHAJLA PETROVICA-ALASA 12-14 VINCA, 11037 BELGRADE. POB 522, BELGRADE, 11001, SERBIA, 2019) Kurtoğlu, Ahmet Emin; Bakbak, DeryaThis study proposes a new model for the residual compressive strength of structural lightweight concrete after exposure to elevated temperatures up to 1000 degrees C. For this purpose, a database of residual compressive strengths of fire exposed lightweight concrete was compiled from the literature. Database consisted a total number of 289 data points, used for generating training and testing datasets. Symbolic regression was carried out to generate formulations by accounting for various input parameters such as heating rate, cooling regime, target temperature, water content, aggregate type, and aggregate content. Afterwards, predictions of proposed formulation is compared to experimental results. Statistical evaluations verify that the prediction performance of proposed model is quite high.Öğe Patch load resistance of longitudinally stiffened webs: Modeling via support vector machines(TECHNO-PRESSPO BOX 33, YUSEONG, DAEJEON 305-600, SOUTH KOREA, 2018) Kurtoğlu, Ahmet EminSteel girders are the structural members often used for passing long spans. Mostly being subjected to patch loading, or concentrated loading, steel girders are likely to face sudden deformation or damage e.g., web breathing. Horizontal or vertical stiffeners are employed to overcome this phenomenon. This study aims at assessing the feasibility of a machine learning method, namely the support vector machines (SVM) in predicting the patch loading resistance of longitudinally stiffened webs. A database consisting of 162 test data is utilized to develop SVM models and the model with best performance is selected for further inspection. Existing formulations proposed by other researchers are also investigated for comparison. BS5400 and other existing models (model I, model II and model III) appear to yield underestimated predictions with a large scatter; i.e., mean experimental-to-predicted ratios of 1.517, 1.092, 1.155 and 1.256, respectively; whereas the selected SVM model has high prediction accuracy with significantly less scatter. Robust nature and accurate predictions of SVM confirms its feasibility of potential use in solving complex engineering problems.Öğe Predicting the Shear Strength of Fiber Reinforced Concrete Corbels Via Support Vector Machines(Sivas Cumhuriyet Üniversitesi, 2018) Kurtoğlu, Ahmet EminPrecast corbels are commonly preferred structural members in industrial buildings. In this study, a novel application of support vector machines (SVM) is employed for the prediction of ultimate shear strength of fiber reinforced corbels, for the first time in literature. SVM models are developed and analyzed using a database of available test results in literature. Predictions of the selected model are compared against the test results and those of available model proposed by Fattuhi (1994). Proposed model has the capability to predict the shear strength of both steel fiber reinforced concrete (SFRC) and glass fiber reinforced concrete (GFRC) corbels. Additionally, a parametric study with a wide range of variables is carried out to test the effect of each parameter on the shear strength. The results confirm the high prediction capacity of proposed model.
