Civil Engineering / İnşaat Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/13
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Article Citation - WoS: 72Citation - Scopus: 79Artificial Neural Networks for Estimating Daily Total Suspended Sediment in Natural Streams(IWA Publishing, 2006) Tayfur, Gökmen; Güldal, VeyselEstimates of sediment loads in natural streams are required for a wide spectrum of water resources engineering problems from optimal reservoir design to water quality in lakes. Suspended sediment constitutes 75-95% of the total load. The nonlinear problem of suspended sediment estimation requires a nonlinear model. An artificial neural network (ANN) model has been developed to predict daily total suspended sediment (TSS) in rivers. The model is constructed as a three-layer feedforward network using the back-propagation algorithm as a training tool. The model predicts TSS rates using precipitation (P) data as input. For network training and testing 240 sets of data sets were used. The model successfully predicted daily TSS loads using the present and past 4 days precipitation data in the input vector with R2 = 0.91 and MAE = 34.22 mg/L. The performance of the model was also tested against the most recently developed non-linear black box model based upon two-dimensional unit sediment graph theory (2D-USGT). The comparison of results revealed that the ANN has a significantly better performance than the 2D-USGT. Investigation results revealed that the ANN model requires a period of more than 75 d of measured P-TSS data for training the model for satisfactory TSS estimation. The statistical parameter range (xmin - xmax) plays a major role for optimal partitioning of data into training and testing sets. Both sets should have comparable values for the range parameter.Article Citation - WoS: 6Citation - Scopus: 7Lateral Stiffness of Steel Bridge I-Girders Braced by Metal Deck Forms(American Society of Civil Engineers (ASCE), 2009) Eğilmez, Oğuz Özgür; Herman, Reagan S.; Helwig, Todd A.The lateral-torsional buckling capacity of steel bridge girders is often increased by incorporating bracing along the girder length. Permanent metal deck forms (PMDF) that are used to support the wet concrete deck during bridge construction are a likely source of stability bracing; however, their bracing performance is greatly limited by flexibility in the connections currently used with the formwork. This paper outlines results from a research study that assessed and improved the bracing potential of metal deck forms used in bridge applications. The research study included shear tests of PMDF panels, and also lateral displacement and buckling tests of twin girder systems braced with PMDF. This paper will provide key results from the shear panel tests and then focus on the lateral displacement tests. Parametric investigations of PMDF bracing behavior were conducted using finite-element analyses and the results from the lateral displacement tests served a critical role in calibrating the finite element models. This paper documents key results from lateral load tests of 17 girder-PMDF systems using a variety of bracing details and PMDF thickness values. © 2009 ASCEArticle Citation - WoS: 24Citation - Scopus: 24GA-optimized model predicts dispersion coefficient in natural channels(IWA Publishing, 2009) Tayfur, GökmenModels whose parameters were optimized by genetic algorithm (GA) were developed to predict the longitudinal dispersion coefficient in natural channels. Following the existing equations in the literature, ten different linear and nonlinear models were first constructed. The models relate the dispersion coefficient to flow and channel characteristics. The GA model was then employed to find the optimal values of the constructed model parameters by minimizing the mean absolute error function (objective function). The GA model utilized an 80% cross-over rate and 4% mutation rate. It started each computation with a population of 100 chromosomes in the gene pool. For each model, while minimizing the objective function, the values of the model parameters were constrained between [-10, +10] at each iteration. The optimal values of the model parameters were obtained using a calibration set of 54 out of 80 sets of measured data. The minimum error was obtained for the case where the model was a linear equation relating dispersion coefficient to flow discharge. The model performance was then satisfactorily tested against the remaining 26 measured validation datasets. It performed better than the existing equations. it yielded minimum errors of MAE = 21.4m2/s (mean absolute error) and RMSE = 28.5m2/s (root mean-squares error) and a maximum accuracy rate of 81%. © IWA Publishing 2009.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: 15Citation - Scopus: 18Influence of Stratification and Shoreline Erosion on Reservoir Sedimentation Patterns(American Society of Civil Engineers (ASCE), 2007) Elçi, Şebnem; Work, Paul A.; Hayter, Earl J.Sedimentation in the main pool of a deep (maximum depth: 50 m), 227 km2 hydropower reservoir was modeled using a three-dimensional numerical model of hydrodynamics and sedimentation for different wind, inflow, and outflow conditions. Short-term velocity measurements made in the reservoir were used to validate some aspects of the hydrodynamic model. The effects of thermal stratification on sedimentation patterns were investigated, since the reservoir is periodically strongly stratified. Stratification alters velocity profiles and thus affects sedimentation in the reservoir. Sedimentation of reservoirs is often modeled considering only the deposition of sediments delivered by tributaries. However, the sediments eroding from the shorelines can contribute significantly to sedimentation if the shorelines of the reservoir erode at sufficiently high rates or if sediment delivery via tributary inflow is small. Thus, shoreline erosion rates for a reservoir were quantified based on measured fetch, parameterized beach profile shape, and measured wind vectors, and the eroded sediments treated as a source within the sedimentation modeling scheme. The methodology for the prediction of shoreline erosion was calibrated and validated using digital aerial photos of the reservoir taken in different years and indicated approximately 1m/year of shoreline retreat for several locations. This study revealed likely zones of sediment deposition in a thermally stratified reservoir and presented a methodology for integration of shoreline erosion into sedimentation studies that can be used in any reservoir.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.Article Citation - WoS: 10Citation - Scopus: 15Stiffness and Strength of Metal Bridge Deck Forms(American Society of Civil Engineers (ASCE), 2007) Eğilmez, Oğuz Özgür; Helwig, Todd A.; Jetann, Charles A.; Lowery, RichardLight gauge metal sheeting is often utilized in the building and bridge industries for concrete formwork. Although the in-plane stiffness and strength of the metal forms are commonly relied upon for stability bracing in buildings, the forms are generally not considered for bracing in steel bridge construction. The primary difference between the forming systems in the two industries is the method of connection between the forms and girders. In bridge construction, an eccentric support angle is incorporated into the connection details to achieve a uniform slab thickness along the girder length. While the eccentric connection is a benefit for slab construction, the flexible connection limits the amount of bracing provided by the forms. This paper presents results from the first phase of a research study investigating the bracing behavior of metal bridge deck forms. Shear diaphragm tests were conducted to determine the shear stiffness and strength of bridge deck forms, and modified connection details were developed that substantially improve the bracing behavior of the forms. The measured stiffness and strength of diaphragms with the modified connection often met or exceeded the values of diaphragms with conventional noneccentric connections. The experimental results for the diaphragms with the modified connection details dramatically improve the potential for bracing of steel bridge girders by metal deck forms.Article Citation - WoS: 46Citation - Scopus: 49Predicting and Forecasting Flow Discharge at Sites Receiving Significant Lateral Inflow(John Wiley and Sons Inc., 2007) Tayfur, Gökmen; Moramarco, Tommaso; Singh, Vijay P.Two models, one linear and one non-linear, were employed for the prediction of flow discharge hydrographs at sites receiving significant lateral inflow. The linear model is based on a rating curve and permits a quick estimation of flow at a downstream site. The non-linear model is based on a multilayer feed-forward back propagation (FFBP) artificial neural network (ANN) and uses flow-stage data measured at the upstream and downstream stations. ANN predicted the real-time storm hydrographs satisfactorily and better than did the linear model. The results of sensitivity analysis indicated that when the lateral inflow contribution to the channel reach was insignificant, ANN, using only the flow-stage data at the upstream station, satisfactorily predicted the hydrograph at the downstream station. The prediction error of ANN increases exponentially with the difference between the peak discharge used in training and that used in testing. ANN was also employed for flood forecasting and was compared with the modified Muskingum model (MMM). For a 4-h lead time, MMM forecasts the floods reliably but could not be applied to reaches for lead times greater than the wave travel time. Although ANN and MMM had comparable performances for an 8-h lead time, ANN is capable of forecasting floods with lead times longer than the wave travel time.Article Citation - WoS: 53Citation - Scopus: 63Artificial Neural Network (ann) Prediction of Compressive Strength of Vartm Processed Polymer Composites(Elsevier Ltd., 2005) Seyhan, Abdullah Tuğrul; Tayfur, Gökmen; Karakurt, Murat; Tanoğlu, MetinA three layer feed forward artificial neural network (ANN) model having three input neurons, one output neuron and two hidden neurons was developed to predict the ply-lay up compressive strength of VARTM processed E-glass/ polyester composites. The composites were manufactured using fabric preforms consolidated with 0, 3 and 6 wt.% of thermoplastic binder. The learning of ANN was accomplished by a backpropagation algorithm. A good agreement between the measured and the predicted values was obtained. Testing of the model was done within low average error levels of 3.28%. Furthermore, the predictions of ANN model were compared with those obtained from a multi-linear regression (MLR) model. It was found that ANN model has better predictions than MLR model for the experimental data. Also, the ANN model was subjected to a sensitivity analysis to obtain its response. As a result, the ANN model was found to have an ability to yield a desired level of ply-lay up compressive strength values for the composites processed with the addition of the thermoplastic binder.Article Citation - WoS: 75Citation - Scopus: 99Case Study: Finite Element Method and Artificial Neural Network Models for Flow Through Jeziorsko Earthfill Dam in Poland(American Society of Civil Engineers (ASCE), 2005) Gökmen, Tayfur; Swiatek, Dorota; Wita, Andrew; Singh, Vijay Pratap RatapA finite element method (FEM) and an artificial neural network (ANN) model were developed to simulate flow through Jeziorsko earthfill dam in Poland. The developed FEM is capable of simulating two-dimensional unsteady and nonuniform flow through a nonhomogenous and anisotropic saturated and unsaturated porous body of an earthfill dam. For Jeziorsko dam, the FEM model had 5,497 triangular elements and 3,010 nodes, with the FEM network being made denser in the dam body and in the neighborhood of the drainage ditches. The ANN model developed for Jeziorsko dam was a feedforward three layer network employing the sigmoid function as an activator and the back-propagation algorithm for the network learning. 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 ANN model. The two models were calibrated and verified using the piezometer data collected on a section of the Jeziorsko dam. The water levels computed by the models satisfactorily compared with those measured by the piezometers. The model results also revealed that the ANN model performed as good as and in some cases better than the FEM model. This case study offers insight into the adequacy of ANN as well as its competitiveness against FEM for predicting seepage through an earthfill dam body.