Civil Engineering / İnşaat Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/13
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Article Citation - WoS: 10Citation - Scopus: 11Transport Capacity Models for Unsteady and Non-Equilibrium Sediment Transport in Alluvial Channels(Elsevier Ltd., 2012) Tayfur, Gökmen; Singh, Vijay P.This study investigates transport capacity models based on different dominant variables-shear stress, stream power, unit stream power, flow discharge, flow velocity, and energy slope - in a model of unsteady and non-equilibrium sediment transport in alluvial channels. The model simulates fully coupled system of water flow, suspended sediment, and bed load sediment transport processes in two-layer system of water flow phase and movable bed. The model employs conservation of mass equation for the water in both the layers; suspended sediment in the water flow phase; sediment in the movable bed layer; and the momentum equation for the water flow in the flow phase. The system is closed by relating the sediment flux in the movable bed layer to the sediment concentration in the same layer by employing the kinematic wave theory. Using the sediment transport capacity expression with different dominant variables, a series of numerical experiments are carried out for unsteady and non-equilibrium sediment transport. The results seem theoretically reasonable for hypothetical cases. The model is calibrated and validated using different experimental data sets. The calibrated value for the transport capacity model's exponent (ki) is found to be 1.50, 1.65, 0.24, 0.56, 4.80, and 0.22 for shear stress, stream power, unit stream power, discharge, velocity, and slope approaches, respectively. The numerical investigation results show that transport capacity model based on any dominant variable can be employed for modelling unsteady and non-equilibrium sediment transport.Article Citation - WoS: 28Citation - Scopus: 30Principle Component Analysis in Conjuction With Data Driven Methods for Sediment Load Prediction(Springer Verlag, 2013) Tayfur, Gökmen; Karimi, Yashar; Singh, Vijay P.This study investigates sediment load prediction and generalization from laboratory scale to field scale using principle component analysis (PCA) in conjunction with data driven methods of artificial neural networks (ANNs) and genetic algorithms (GAs). Five main dimensionless parameters for total load are identified by using PCA. These parameters are used in the input vector of ANN for predicting total sediment loads. In addition, nonlinear equations are constructed, based upon the same identified dimensionless parameters. The optimal values of exponents and constants of the equations are obtained by the GA method. The performance of the so-developed ANN and GA based methods is evaluated using laboratory and field data. Results show that the expert methods (ANN and GA), calibrated with laboratory data, are capable of predicting total sediment load in field, thus showing their transferability. In addition, this study shows that the expert methods are not transferable for suspended load, perhaps due to insufficient laboratory data. Yet, these methods are able to predict suspended load in field, when trained with respective field data.Article Citation - WoS: 22Citation - Scopus: 29Predicting Mean and Bankfull Discharge From Channel Cross-Sectional Area by Expert and Regression Methods(Springer Verlag, 2011) Tayfur, Gökmen; Singh, Vijay P.This study employed four methods-non-linear regression, fuzzy logic (FL), artificial neural networks (ANNs), and genetic algorithm (GA)-based nonlinear equation-for predicting mean discharge and bank-full discharge from cross-sectional area. The data compiled from the literature were separated into two groups-training (calibration) and testing (verification). Using training data sets, the methods were calibrated to obtain optimal values of the coefficients of the non-linear regression method; optimal number of fuzzy subsets, their base widths and fuzzy rules for the fuzzy method; and the optimal number of neurons in the hidden layer, the learning rate and momentum factor values for the ANN model. The GA-based method employed 100 chromosomes in the initial gene pool, 80% cross over rate and 4% mutation rate in determining the optimal values of the coefficients of the constructed nonlinear equation. The calibrated methods were then applied to the test data sets. The test results showed that the non-linear regression, ANN and GA-based methods were comparable in predicting the mean discharge while the fuzzy method produced high errors and low accuracy. The GA-based method had the highest accuracy of 75%. In terms of predicting bankfull discharge, all methods produced satisfactory results, although the fuzzy method had the lowest accuracy of 33%. The results of sensitivity analysis, which is limited to the GA-based and nonlinear regression methods, showed that the GA-based method calibrated with low bankfull discharge values can be successfully applied to predict high bankfull discharge values. This has important implications for predicting bankfull rates at ungauged sites. On the other hand, the sensitivity analysis results also showed that both the non-linear regression and GA-based methods have poor extrapolation capability for predicting mean discharge data.Article Citation - WoS: 16Citation - Scopus: 18Kinematic Wave Model of Bed Profiles in Alluvial Channels(John Wiley and Sons Inc., 2006) Tayfur, Gökmen; Singh, Vijay P.A mathematical model, based on the kinematic wave (KW) theory, is developed for describing the evolution and movement of bed profiles in alluvial channels. The model employs a functional relation between sediment transport rate and concentration, a relation between flow velocity and depth and Velikanov's formula relating suspended sediment concentration to flow variables. Laboratory flume and field data are used to test the model. Transient bed profiles in alluvial channels are also simulated for several hypothetical cases involving different water flow and sediment concentration characteristics. The model-simulated bed profiles are found to be in good agreement with what is observed in the laboratory, and they seem theoretically reasonable for hypothetical cases. The model results reveal that the mean particle velocity and maximum concentration (maximum bed form elevation) strongly affect transient bed profiles.Article Citation - WoS: 17Citation - Scopus: 19Kinematic Wave Model for Transient Bed Profiles in Alluvial Channels Under Nonequilibrium Conditions(John Wiley and Sons Inc., 2007) Tayfur, Gökmen; Singh, Vijay P.Transient bed profiles in alluvial channels are generally modeled using diffusion (or dynamic) waves and assuming equilibrium between detachment and deposition rates. Equilibrium sediment transport can be considerably affected by an excess (or deficiency) of sediment supply due to mostly flows during flash floods or floods resulting from dam break or dike failure. In such situations the sediment transport process occurs under nonequilibrium conditions, and extensive changes in alluvial river morphology can take place over a relatively short period of time. Therefore the study and prediction of these changes are important for sustainable development and use of river water. This study hence developed a mathematical model based on the kinematic wave theory to model transient bed profiles in alluvial channels under nonequilibrium conditions. The kinematic wave theory employs a functional relation between sediment transport rate and concentration, the shear-stress approach for flow transport capacity, and a relation between flow velocity and depth. The model satisfactorily simulated transient bed forms observed in laboratory experiments.Article Citation - WoS: 68Citation - Scopus: 86Fuzzy Logic Algorithm for Runoff-Induced Sediment Transport From Bare Soil Surfaces(Elsevier Ltd., 2003) Tayfur, Gökmen; Özdemir, Serhan; Singh, Vijay P.Utilizing the rainfall intensity, and slope data, a fuzzy logic algorithm was developed to estimate sediment loads from bare soil surfaces. Considering slope and rainfall as input variables, the variables were fuzzified into fuzzy subsets. The fuzzy subsets of the variables were considered to have triangular membership functions. The relations among rainfall intensity, slope, and sediment transport were represented by a set of fuzzy rules. The fuzzy rules relating input variables to the output variable of sediment discharge were laid out in the IF-THEN format. The commonly used weighted average method was employed for the defuzzification procedure. The sediment load predicted by the fuzzy model was in satisfactory agreement with the measured sediment load data. Predicting the mean sediment loads from experimental runs, the performance of the fuzzy model was compared with that of the artificial neural networks (ANNs) and the physics-based models. The results of showed revealed that the fuzzy model performed better under very high rainfall intensities over different slopes and over very steep slopes under different rainfall intensities. This is closely related to the selection of the shape and frequency of the fuzzy membership functions in the fuzzy model.