Fuat Demir

Total salpingectomy during abdominal hysterectomy: Effects on ovarian reserve and ovarian stromal blood flow

Aim:  To investigate the short‐ and medium‐term consequences of performing total salpingectomy during abdominal hysterectomy (without oophorectomy) on certain ovarian reserve parameters and blood flow velocity measurements through the ovarian stroma.

Stress–strain modeling of high-strength concrete by the adaptive network-based fuzzy inference system (ANFIS) approach

AbstractIn this study, an adaptive network-based fuzzy inference system (ANFIS) approach is presented for modeling of high-strength concrete under uniaxial loading. The ANFIS approach applied to test the data of concrete cylinder test is available in the literature. In this paper, the stress–strain behavior of high-strength concrete subjected to axial load is obtained by using the ANFIS model. It is shown that the present model can predict the stress–strain behavior of concrete accurately by taking into account the effective parameters. The advantage of the proposed approach is that the stress–strain behavior of high-strength concrete can be predicted easily. The results of ANFIS approach are compared with the analytical models given in various studies concerning cylinder tests. The ANFIS approach results given show a good agreement with the experimental results.

Comparison of critical column buckling load in regression, fuzzy logic and ANN based estimations

In structural stability analyses, determining the critical buckling load is a crucial issue. Regression, fuzzy logic and Artificial Neural Network ANN algorithms can be used to determine critical buckling loads. This study compares the results of different approaches for column buckling load prediction. Regression, Fuzzy logic and ANN algorithms were employed in the analyses, representing material properties to take uncertainties into account and the results were compared. The results show that uncertainties play an important role in stability analyses and should be considered in the design. The elastic modulus results predicted by regression, fuzzy logic and ANN are also compared to those obtained using empirical results of the buildings codes and various models. These comparisons show that obtained results in the present study give closer results than the different design codes. Therefore, proposed models can be used for critical buckling loads and regression, fuzzy logic and ANN have strong potential as a feasible tool for estimating column buckling loads within the range of input parameters considered.

Determination of elastic moduli effects on storey-drifts by fuzzy logic algorithm

Concrete compression strength is a standard input for obtaining the elastic modulus of concrete. Calculated values of elastic modulus for the same concrete strength may vary according to different codes throughout the world since the formulas of the elastic modulus are different. Elastic modulus is one of the important parameters for structural design. Storey-drifts are also among the important structural design criteria directly related to the elastic modulus. Different elastic moduli values may lead lateral storey-drift calculations to differ from the code requirements. More dependable elastic moduli definition results in more dependable storey-drift calculations. Fuzzy logic algorithm, which generates more dependable results in engineering problems, is applicable to solve this problem. This study investigates the effects of storey-drifts on structural design; upper and lower boundaries of elastic moduli are predicted using a fuzzy logic algorithm in order to determine the effects of the elastic moduli on the storey-drift limitations found in different design codes. The study considers various design codes that are obtained worldwide. Study results also show that using a range of values for the elastic moduli is a more dependable approach for structural design safety, and that dependable storey-drifts are crucially important for structural design.

Ground Motion Data Profile of Western Turkey with Intelligent Hybrid Processing

The recent earthquakes caused severe damages on the existing buildings. By this motivation, an important amount of research work has been conducted to determine the seismic risk of seismically active regions. For an accurate seismic risk assessment, processing of ground motions would provide an advantage. Using the current technology, it is not possible to precisely predict the future earthquakes. Therefore, most of the current seismic risk assessment methodologies are based on statistical evaluation by using recurrence and magnitude of the earthquakes hit the specified region. Because of the limited number of records on earthquakes, the quality of definitions is questionable. Fuzzy logic algorithm can be used to improve the quality of the definition. In the present study, ground motion data profile of western Turkey is defined using an intelligent hybrid processing. The approach is given in a practical way for an easier and faster calculation. Earthquake data between 1970 and 1999 from western part of Turkey have been used for training. The results are tested and validated with the earthquake data between 2000 and 2015 of the same region. Enough approximation was validated between calculated values and the earthquake data by using the intelligent hybrid processing.