WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
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Article Closure To "reverse Flood Routing in Rivers Using Linear and Nonlinear Muskingum Models" by Meisam Badfar, Reza Barati, Emrah Dogan, and Gokmen Tayfur(American Society of Civil Engineers (ASCE), 2022) Badfar, Meisam; Barati, Reza; Doğan, Emrah; Tayfur, GökmenArticle Citation - WoS: 46Citation - Scopus: 53Distorted Physical Model To Study Sudden Partial Dam Break Flows in an Urban Area(American Society of Civil Engineers (ASCE), 2014) Güney, Mehmet Şükrü; Tayfur, Gökmen; Bombar, Gökçen; Elçi, ŞebnemA distorted physical model, based on Ürkmez Dam in Izmir, Turkey, was built to study sudden partial dam break flows. The distorted model had a horizontal scale of 1/150 and a vertical scale of 1/30, containing dam reservoir, dam body, and downstream area-from dam body to Ürkmez urban area until the sea coast. In the model, the reservoir is approximately 12 m3, the dam body has a width of 2.84 m and a height of 1.07 m, and the downstream area is nearly 200 m2. The Ürkmez Dam was chosen because Ürkmez Town is located right at its downstream area, allowing the study of dam break flows in an urban area. Furthermore, the dimensions were suitable such that it allowed the construction of a physical model (dam reservoir, dam body, and downstream area) having a horizontal scale of 1/150 in the available space of 300 m2. The features creating roughness such as buildings, bridge, and roads were also reflected in the physical model. The dam break flow was investigated for sudden partial collapse, which was simulated by a trapezoidal breach on the dam body. The water depths at downstream area were measured at eight different locations by using e+ WATER L (level) sensors. The velocities were measured at four different locations by ultrasonic velocity profiler (UVP) transducers. The propagation of the flood was recorded by a high-defnition camera. The experimental results show that the Ürkmez area can be flooded in a matter of minutes, at depths reaching up to 3 m in residential areas in 4 min. The flood wave front can reach the residential areas in 2 min and to the sea coast in 4 min. Flow velocities can reach 70.9 km/h in sparse residential areas, close to dam body. Away from the dam body in the sparse buildings part of the town, the velocities can reach 27.7 km/h. In dense residential areas of the town, the velocities are too low (2.8 km/h) but flow depths can reach 3 m. Velocity profiles show similar behavior like unsteady and nonuniform open channel flow in nonresidential areas close to the dam body. In residential areas away from the dam body, the velocity profiles are more uniform, having lower velocity values. Vertical variations of velocities show markedly different behavior during rising and recession stages. The profiles are smooth during the rising stage in sparse residential area, yet it shows fluctuating behavior during the recession stage.Article Citation - WoS: 54Citation - Scopus: 69Strength 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, ÖnderHigh-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: 3Citation - Scopus: 3Simulating Transient Sediment Waves in Aggraded Alluvial Channels by Double-Decomposition Method(American Society of Civil Engineers (ASCE), 2011) Tayfur, Gökmen; Singh, Vijay P.By using the double-decomposition (DD) method, this study simulates transient sediment waves caused by aggradation described by a diffusion-type partial differential equation (PDE). The DD method solves the PDE by decomposing the solution function for sediment rate into a summation of M number of components, where M stands for the order of approximation. The solution was approximated by considering only the first three terms. The model satisfactorily simulated laboratory-measured aggradation bed profiles with, on average, a mean absolute error (MAE) of 0.70 cm, a root-mean-square error (RMSE) of 0.84 cm, a mean relative error (MRE) of 1.11%, and R2=0.95. The model performance was also tested by using numerical and error-function solutions. In addition, the results obtained from application of the DD solution to hypothetical field cases were found to be theoretically compatible with what may be observed in natural streams. However, sediment wave fronts in later periods of the simulation time reached equilibrium bed levels more quickly, around in the middle section of the channel.Article Citation - WoS: 60Citation - Scopus: 69Experimental and Numerical Investigation of Bed-Load Transport Under Unsteady Flows(American Society of Civil Engineers (ASCE), 2011) Bombar, Gökçen; Elçi, Şebnem; Tayfur, Gökmen; Güney, M. Şükrü; Bor, AslıThe dynamic behavior of bed-load sediment transport under unsteady flow conditions is experimentally and numerically investigated. A series of experiments are conducted in a rectangular flume (18 m in length, 0.80 m in width) with various triangular and trapezoidal shaped hydrographs. The flume bed of 8 cm in height consists of scraped uniform small gravel of D 50=4.8 mm. Analysis of the experimental results showed that bed-load transport rates followed the temporal variation of the triangular and trapezoidal hydrographs with a time lag on the average of 11 and 30 s, respectively. The experimental data were also qualitatively investigated employing the unsteady-flow parameter and total flow work index. The analysis results revealed that total yield increased exponentially with the total flow work. An original expression which is based on the net acceleration concept was proposed for the unsteadiness parameter. Analysis of the results then revealed that the total yield increased exponentially with the increase in the value of the proposed unsteadiness parameter. Further analysis of the experimental results revealed that total flow work has an inverse exponential variation relation with the lag time. A one-dimensional numerical model that employs the governing equations for the conservation of mass for water and sediment and the momentum was also developed to simulate the experimental results. The momentum equation was approximated by the diffusion wave approach, and the kinematic wave theory approach was employed to relate the bed sediment flux to the sediment concentration. The model successfully simulated measured sedimentographs. It predicted sediment yield, on the average, with errors of 7% and 15% of peak loads for the triangular and trapezoidal hydrograph experiments, respectively.Article Citation - WoS: 15Citation - Scopus: 17Genetic Algorithm-Based Discharge Estimation at Sites Receiving Lateral Inflows(American Society of Civil Engineers (ASCE), 2009) Tayfur, Gökmen; Barbetta, Silvia; Moramarco, TommasoThe genetic algorithm (GA) technique is applied to obtain optimal parameter values of the standard rating curve model (RCM) for predicting, in real time, event-based flow discharge hydrographs at sites receiving significant lateral inflows. The standard RCM uses the information of discharge and effective cross-sectional flow area at an upstream station and effective cross-sectional flow area wave travel time later at a downstream station to predict the flow rate at this last site. The GA technique obtains the optimal parameter values of the model, here defined as the GA-RCM model, by minimizing the mean absolute error objective function. The GA-RCM model was tested to predict hydrographs at three different stations, located on the Upper Tiber River in central Italy. The wave travel times characterizing the three selected river branches are, on the average, 4, 8, and 12h. For each river reach, seven events were employed, four for the model parameters' calibration and three for model testing. The GA approach, employing 100 chromosomes in the initial gene pool, 75% crossover rate, 5% mutation rate, and 10,000 iterations, made the GA-RCM model successfully simulate the hydrographs observed at each downstream section closely capturing the trend, time to peak, and peak rates with, on the average, less than 5% error. The model performance was also tested against the standard RCM model, which uses, on the contrary to the GA-RCM model, different values for the model parameters and wave travel time for each event, thus, making the application of the standard RCM for real time discharge monitoring inhibited. The comparative results revealed that the RCM model improved its performance by using the GA technique in estimating parameters. The sensitivity analysis results revealed that at most two events would be sufficient for the GA-RCM model to obtain the optimal values of the model parameters. A lower peak hydrograph can also be employed in the calibration to predict a higher peak hydrograph. Similarly, a shorter travel time hydrograph can be used in GA to obtain optimal model parameters that can be used to simulate floods characterized by longer travel time. For its characteristics, the GA-RCM model is suitable for the monitoring of discharge in real time, at river sites where only water levels are observed.Article Citation - WoS: 37Citation - Scopus: 49Predicting Suspended Sediment Loads and Missing Data for Gediz River, Turkey(American Society of Civil Engineers (ASCE), 2009) Ülke, Aslı; Tayfur, Gökmen; Özkul, SevinçPrediction of suspended sediment load (SSL) is important for water resources quantity and quality studies. The SSL of a stream is generally determined by direct measurement of the suspended sediment concentration or by employing sediment rating curve method. Although direct measurement is the most reliable method, it is very expensive, time consuming, and, in many instances, problematic for inaccessible sections, especially during floods. On the other hand, measuring precipitation and flow discharge is relatively easier and hence, there are more rain and flow gauging stations than SSL gauging stations in Turkey. Furthermore, due to its cost, measurements of SSL are carried out in longer periods compared to precipitation and flow measurements. Although daily precipitation and flow measurements are available for most of the Turkish river basins, at best semimonthly measurements are available for SSL. As such, it is essential to predict SSL from precipitation and flow data and to fill the gap for the missing data records. This study employed artificial intelligence methods of artificial neural networks (ANN) and neurofuzzy inference system, the sediment rating curve method, multilinear regression, and multinonlinear regression methods for this purpose. The comparative analysis of the results showed that the artificial intelligence methods have superiority over the other methods for predicting semimonthly suspended sediment loads. The ANN using conjugate gradient optimization method showed the best performance among the proposed models. It also satisfactorily generated daily SSL data for the missing period record of Gediz River, Turkey.Article Citation - WoS: 5Citation - Scopus: 5Kinematic Wave Theory for Transient Bed Sediment Waves in Alluvial Rivers(American Society of Civil Engineers (ASCE), 2008) Singh, Vijay P.; Tayfur, GökmenTransient bed sediment waves in alluvial rivers have been described using a multitude of hydraulic formulations. These formulations are based on some form of the St. Venant equations and conservation of mass of sediment in suspension and in bed. Depending on the assumptions employed, a hierarchy of formulations is expressed. These formulations in the literature employ uncoupled, semicoupled, or fully coupled transport models treating the sediment waves as either hyperbolic (dynamic wave) or parabolic (diffusion wave). It is, however, hypothesized that the movement of bed sediment waves in alluvial rivers can be described as a kinematic wave. Kinematic wave theory employs a functional relation between sediment transport rate and concentration and a relation between flow velocity and depth. This study summarizes the hierarchy of the formulations while emphasizing the kinematic wave theory for describing transient bed sediment waves. The applicability of the theory is shown for laboratory flume data and hypothetical cases.Annotation Citation - WoS: 1Citation - Scopus: 1Closure To "ann and Fuzzy Logic Models for Simulating Event-Based Rainfall-Runoff" by Gokmen Tayfur and Vijay P. Singh(American Society of Civil Engineers (ASCE), 2008) Tayfur, Gökmen; Singh, Vijay P.We would like to thank Dr. Wong for his interest in and thoughts on our analysis of runoff hydrograph prediction and the goodnessof-fit measurement. We agree that visual comparison of simulated and measured hydrographs is an important indicator for assessing the performance of models. Visual inspection allows one to see intricate differences between hydrographs.Article Citation - WoS: 103Citation - Scopus: 126Ann and Fuzzy Logic Models for Simulating Event-Based Rainfall-Runoff(American Society of Civil Engineers (ASCE), 2006) Tayfur, Gökmen; Singh, Vijay P.This study presents the development of artificial neural network (ANN) and fuzzy logic (FL) models for predicting event-based rainfall runoff and tests these models against the kinematic wave approximation (KWA). A three-layer feed-forward ANN was developed using the sigmoid function and the backpropagation algorithm. The FL model was developed employing the triangular fuzzy membership functions for the input and output variables. The fuzzy rules were inferred from the measured data. The measured event based rainfall-runoff peak discharge data from laboratory flume and experimental plots were satisfactorily predicted by the ANN, FL, and KWA models. Similarly, all the three models satisfactorily simulated event-based rainfall-runoff hydrographs from experimental plots with comparable error measures. ANN and FL models also satisfactorily simulated a measured hydrograph from a small watershed 8.44 km2 in area. The results provide insights into the adequacy of ANN and FL methods as well as their competitiveness against the KWA for simulating event-based rainfall-runoff processes.