Tayfur, Gökmen

Profile URL
https://hdl.handle.net/11147/15861
Name Variants
Tayfur, G.
Tayfur, Gokmen
Tayfur, G
Job Title
Email Address
gokmentayfur@iyte.edu.tr
Main Affiliation
03.03. Department of Civil Engineering
Status
Current Staff
Website
https://civil.iyte.edu.tr/wp-content/uploads/sites/87/2019/10/TayfurCV.pdf
ORCID ID
0000-0001-9712-4031
Scopus Author ID
6701638605
Turkish CoHE Profile ID
Google Scholar ID
lEI9DuwAAAAJ
WoS Researcher ID

Sustainable Development Goals

NO POVERTY1
NO POVERTY
4
Research Products
ZERO HUNGER2
ZERO HUNGER
12
Research Products
GOOD HEALTH AND WELL-BEING3
GOOD HEALTH AND WELL-BEING
1
Research Products
QUALITY EDUCATION4
QUALITY EDUCATION
4
Research Products
GENDER EQUALITY5
GENDER EQUALITY
1
Research Products
CLEAN WATER AND SANITATION6
CLEAN WATER AND SANITATION
31
Research Products
AFFORDABLE AND CLEAN ENERGY7
AFFORDABLE AND CLEAN ENERGY
12
Research Products
DECENT WORK AND ECONOMIC GROWTH8
DECENT WORK AND ECONOMIC GROWTH
8
Research Products
INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
18
Research Products
REDUCED INEQUALITIES10
REDUCED INEQUALITIES
1
Research Products
SUSTAINABLE CITIES AND COMMUNITIES11
SUSTAINABLE CITIES AND COMMUNITIES
23
Research Products
RESPONSIBLE CONSUMPTION AND PRODUCTION12
RESPONSIBLE CONSUMPTION AND PRODUCTION
12
Research Products
CLIMATE ACTION13
CLIMATE ACTION
21
Research Products
LIFE BELOW WATER14
LIFE BELOW WATER
9
Research Products
LIFE ON LAND15
LIFE ON LAND
9
Research Products
PEACE, JUSTICE AND STRONG INSTITUTIONS16
PEACE, JUSTICE AND STRONG INSTITUTIONS
1
Research Products
PARTNERSHIPS FOR THE GOALS17
PARTNERSHIPS FOR THE GOALS
0
Research Products
Documents

135

Citations

3476

h-index

34

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This researcher does not have a WoS ID.
Scholarly Output

168

Articles

117

Views / Downloads

349748/69922

Supervised MSc Theses

19

Supervised PhD Theses

5

WoS Citation Count

2576

Scopus Citation Count

3073

Patents

0

Projects

2

WoS Citations per Publication

15.33

Scopus Citations per Publication

18.29

Open Access Source

128

Supervised Theses

24

JournalCount
Water Resources Management13
Journal of Hydrologic Engineering9
Journal of Hydrology8
Environmental Monitoring and Assessment7
Pure and Applied Geophysics5
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Scholarly Output Search Results

Now showing 1 - 10 of 168
  • Article
    Citation - WoS: 54
    Citation - Scopus: 69
    Strength Prediction of High-Strength Concrete by Fuzzy Logic and Artificial Neural Networks
    (American Society of Civil Engineers (ASCE), 2014) Tayfur, Gökmen; Erdem, Tahir Kemal; Kırca, Önder
    High-strength concretes (HSC) were prepared with five different binder contents, each of which had several silica fume (SF) ratios (0-15%). The compressive strength was determined at 3, 7, and 28 days, resulting in a total of 60 sets of data. In a fuzzy logic (FL) algorithm, three input variables (SF content, binder content, and age) and the output variable (compressive strength) were fuzzified using triangular membership functions. A total of 24 fuzzy rules were inferred from 60% of the data. Moreover, the FL model was tested against an artificial neural networks (ANNs) model. The results show that FL can successfully be applied to predict the compressive strength of HSC. Three input variables were sufficient to obtain accurate results. The operators used in constructing the FL model were found to be appropriate for compressive strength prediction. The performance of FL was comparable to that of ANN. The extrapolation capability of FL and ANNs were found to be satisfactory.
  • Article
    Citation - WoS: 5
    Citation - Scopus: 7
    Experimental and Modeling Study of Strength of High Strength Concrete Containing Binary and Ternary Binders
    (Foundation Cement, Lime, Concrete, 2011) Erdem, Tahir Kemal; Tayfur, Gökmen; Kırca, Önder
    Silica fume (SF), fl y ash (FA) and ground granulated blastfurnace slag (S) are among the most widely utilized mineral additions for normal strength concrete (NSC) and high strength concrete (HSC). High Reactivity Metakaolin (HRMK) is a relatively new mineral addition, produced by calcination of highly pure kaolin. The replacement of cement with HRMK increases the strength, especially at early ages, and improves durability of concrete. (1-3). Pumice (P) is a porous volcanic glass containing 60-75 SiO2% and 13-17% Al2O3. When fi nely ground, it shows pozzolanic characteristics but it is generally used as a lightweight aggregate in the concrete industry (4, 5). HRMK and P have white color and, therefore, are useful for production of white concrete when applied with white Portland cement (WPC)
  • Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Estimation of Mechanical Properties of Limestone Using Regression Analyses and Ann
    (Foundation Cement, Lime, Concrete, 2012) Teomete, Egemen; Tayfur, Gökmen; Aktaş, Engin
    Estimation of mechanical properties of rocks is important for researchers and field engineers working in cement and concrete industry. Limestone is used in cement production. In this study, Schmidt hammer, ultrasonic pulse velocity, porosity, uniaxial compression and indirect tension tests were conducted on limestone obtained from a historical structure. Regression analyses were used to develop models relating mechanical properties of limestone. Artificial Neural Network (ANN) was performed to determine the mechanical properties. The performance of regression models and ANN were compared by existing models in the literature. The results showed that the regression models and ANN yield satisfactory performance with minimum error. The regression models between tensile strength and wave velocity, tensile strength and porosity, wave velocity and porosity have been developed for the first time in literature. The ANN is used for the first time to estimate the mechanical properties of limestone. The use of separate training and testing sets in the regression analyses of mechanical properties of limestone is conducted for the first time. The models developed in this study can be used by researchers and field engineers to relate the mechanical properties of limestone.
  • Conference Object
    Modflow-2005 Nümerik Modelleme Metodu ile Yeraltısuyu Potansiyelinin Değerlendirilmesi: Alaşehir (manisa) Örneği
    (TMMOB Jeoloji Mühendisleri Odası, 2018) Özdayı, Murat Ozan; Şimşek, Celalettin; Tayfur, Gökmen; Baba, Alper
    Dünyada yaşanan iklim değişikliği, aşırı nüfus artışı ve Türkiye gibi gelişmekte olan ülkelerde endüstrinin artması ile birlikte su kaynaklarının sürdürülebilir kullanımı önem kazanmıştır. Yüzey sularında kirlilik ve miktar açısından yaşanan problemler nedeniyle, tüm alanlarda yeraltısularının tüketimi artmaktadır. Ancak yüzey suları kadar kendini hızlı yenilemeyen yeraltısuları sürdürülebilirlik açısından çok büyük tehdit altındadır. Özellikle Türkiye yaşanan yarı-kurak iklim nedeniyle bu sorunlardan çok fazla etkilenmektedir. Bu nedenle yeraltısularının kontrol altına alınması zorunlu hale gelmiştir. Yeraltısularının kontrol altına alınmasında yapılacak olan çalışmalarda en önemli konulardan biri olan yeraltısuyu beslenim mekanizmasının belirlenmesidir. Beslenim mekanizmasının ortaya konulması ile yeraltısuyu kullanımları kontrol altına alınarak, geleceğe yönelik çalışmalar daha doğru ve kapsamlı hale gelecektir. Bu çalışma kapsamında Gediz alt havzası olan Alaşehir Havzası çalışılmıştır. Havzadaki alüvyon akiferdeki yeraltısuyu potansiyeli MODFLOW-2005 nümerik modellemesi ile değerlendirilmiştir.
  • Master Thesis
    Studying Seepage in a Body of Earth-Fill Dam by (artifical Neural Networks) Anns
    (Izmir Institute of Technology, 2006) Ersayın, Deniz; Tayfur, Gökmen; Tayfur, Gökmen
    Dams are structures that are used especially for water storage , energy production, and irrigation. Dams are mainly divided into four parts on the basis of the type and materials of construction as gravity dams, buttress dams, arch dams, and embankment dams. There are two types of embankment dams: earthfill dams and rockfill dams. In this study, seepage through an earthfill dam's body is investigated using an artificial neural network model. Seepage is investigated since seepage both in the dam's body and under the foundation adversely affects dam's stability. This study specifically investigated seepage in dam.s body. The seepage in the dams body follows a phreatic line. In order to understand the degree of seepage, it is necessary to measure the level of phreatic line. This measurement is called as piezometric measurement. Piezometric data sets which are collected from Jeziorsko earthfill dam in Poland were used for training and testing the developed ANN model. Jeziorsko dam is a non-homogeneous earthfill dam built on the impervious foundation. Artificial Neural Networks are one of the artificial intelligence related technologies and have many properties. In this study the water levels on the upstream and downstream sides of the dam were input variables and the water levels in the piezometers were the target outputs in the artificial neural network model. In the line of the purpose of this research, the locus of the seepage path in an earthfill dam is estimated by artificial neural networks. MATLAB 6 neural network toolbox is used for this study.
  • Article
    Citation - WoS: 7
    Citation - Scopus: 5
    Meteorological Drought and Trend Effects on Transboundary River Basins in Afghanistan
    (Springer, 2023) Hayat, Ehsanullah; Tayfur, Gökmen
    Afghanistan, as a landlocked country located within central and southwestern Asia, has an arid to semi-arid climate. Most of the people are involved in agricultural activities, and a major part of the country's gross domestic product depends on agriculture, but the country has the lowest water storage capacity. Consecutive periods of drought and rapid snowmelt due to climate change have made it more challenging for suitable water resource management practices. This study investigates the historical meteorological drought characteristics across the whole country by employing the Reconnaissance Drought Index for the period 1979-2019 using data from 55 meteorological stations. Trends in precipitation and temperature are also investigated using the Mann-Kendall's and the Sen's slope statistical tests. A four-decadal countrywide drought map is generated. Extreme and severe droughts were observed in 1999 and 2000 across the whole country. Moderate drought events have started to occur with a frequency of 3 to 5 years since 1999. The decadal annual rainfall values in each river basin indicate that rainfall has decreased in the last two decades with a significant decline in 1999-2008. The trends of increase in temperature and decrease in precipitation are indications of rapid climate change in the country, especially in the south, west, and southwest regions. Due to the intensity and frequency of the droughts, river flow rates have decreased; and therefore, there is a need for the upstream and downstream neighboring countries to come to terms with the phenomenon of a new normal in the hydrological cycle and accordingly revise new water sharing treaties.
  • Article
    Citation - WoS: 5
    Citation - Scopus: 5
    Developing Predictive Equations for Water Capturing Performance and Sediment Release Efficiency for Coanda Intakes Using Artificial Intelligence Methods
    (MDPI, 2022) Hazar, Oğuz; Tayfur, Gökmen; Elçi, Şebnem; Singh, Vijay P.
    Estimation of withdrawal water and filtered sediment amounts are important to obtain maximum efficiency from an intake structure. The purpose of this study is to develop empirical equations to predict Water Capturing Performance (WCP) and Sediment Release Efficiency (SRE) for Coanda type intakes. These equations were developed using 216 sets of experimental data. Intakes were tested under six different slopes, six screens, and three water discharges. In SRE experiments, sediment concentration was kept constant. Dimensionless parameters were first developed and then subjected to multicollinearity analysis. Then, nonlinear equations were proposed whose exponents and coefficients were obtained using the Genetic Algorithm method. The equations were calibrated and validated with 70 and 30% of the data, respectively. The validation results revealed that the empirical equations produced low MAE and RMSE and high R2 values for both the WCP and the SRE. Results showed outperformance of the empirical equations against those of MNLR. Sensitivity analysis carried out by the ANNs revealed that the geometric parameters of the intake were comparably more sensitive than the flow characteristics.
  • Conference Object
    Citation - WoS: 1
    Citation - Scopus: 6
    Genetic Algorithm-Artificial Neural Network Model for the Prediction of Germanium Recovery From Zinc Plant Residues
    (Taylor and Francis Ltd., 2002) Akkurt, Sedat; Özdemir, Serhan; Tayfur, Gökmen
    A multi-layer, feed-forward, back-propagation learning algorithm was used as an artificial neural network (ANN) tool to predict the extraction of germanium from zinc plant residues by sulphuric acid leaching. A genetic algorithm (GA) was used for the selection of training and testing data and a GA-ANN model of the germanium leaching system was created on the basis of the training data. Testing of the model yielded good error levels (r2 = 0.95). The model was employed to predict the response of the system to different values of the factors that affect the recovery of germanium and the results facilitate selection of the experimental conditions in which the optimum recovery will be achieved.
  • Master Thesis
    Artificial Neural Networks Model for Air Quality in the Region of Izmir
    (Izmir Institute of Technology, 2002) Birgili, Savaş; Tayfur, Gökmen
    In this study, a systematic approach to the development of the artificial neural networks based forecasting model is presented. S02, and dust values are predicted with different topologies, inputs and transfer functions. Temperature and wind speed values are used as input parameters for the models. The back-propagation learning algorithm is used to train the networks. R 2 (correlation coefficient), and daily average errors are employed to investigate the accuracy of the networks. MATLAB 6 neural network toolbox is used for this study. The study results indicate that the neural networks are able to make accurate predictions even with the limited number of parameters. Results also show that increasing the topology of the network and number of the inputs, increases the accuracy of the network. Best results for the S02 forecasting are obtained with the network with two hidden layers, hyperbolic tangent function as transfer function and three input variables (R2 was found as 0,94 and daily average error was found as 3,6 j..lg/m3).The most accurate results for the dust forecasting are also obtained with the network with two hidden layer, hyperbolic tangent function as transfer function and three input variables (R2 was found as 0,92 and daily average error was found as 3,64 j..lg/m3).S02 and dust predictions using their last seven days values as an input are also studied, and R2 is calculated as 0,94 and daily average error is calculated as 4,03 Jlg/m3 for S02 prediction and R2 is calculated as 0,93 and daily average error is calculated as 4,32 Jlg/m3 for dust prediction and these results show that the neural network can make accurate predictions.
  • Article
    Citation - WoS: 28
    Citation - Scopus: 32
    Describing the Karst Evolution by the Exploitation of Hydrologic Time-Series Data
    (Springer Verlag, 2015) Katsanou, K.; Lambrakis, Nicolaos J.; Tayfur, Gökmen; Baba, Alper
    The importance of the groundwater management of karst aquifers relatively to their complexity requires the knowledge of the subsurface flow and storage behavior. In this study, a methodological approach based on the exploitation of daily spring’s discharge data was developed and tested. The methodology makes use of the hydrograph recession curves, the correlograms output, and the logarithmically structured duration curves. This methodological approach was applied to the complex karst system of Louros basin. The Louros karst system consists of individual karst units discharged by respective springs which are distributed on three levels and form three easily distinguishable groups. The application results revealed a well organized karst system with conduits of slow and fast flow. It also revealed the uniformity and the complexity of the different units, as well as the properties, such as the storativity and the evolutionary process. This approach demonstrates the benefits of interpreting different methods in a hydrologically meaningful way for the recharge data evaluation.