Civil Engineering / İnşaat Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/13
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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: 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: 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: 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.Article Citation - WoS: 27Citation - Scopus: 101Predicting Longitudinal Dispersion Coefficient in Natural Streams by Artificial Neural Network(American Society of Civil Engineers (ASCE), 2005) Tayfur, G; Singh, VPAn artificial neural network (ANN) model was developed to predict the longitudinal dispersion coefficient in natural streams and rivers. The hydraulic variables [flow discharge (Q), flow depth (H), flow velocity (U), shear velocity (u*), and relative shear velocity (U/u*)] and geometric characteristics [channel width (B), channel sinuosity (sigma), and channel shape parameter (beta)] constituted inputs to the ANN model, whereas the dispersion coefficient (K-x) was the target model output. The model was trained and tested using 71 data sets of hydraulic and geometric parameters and dispersion coefficients measured on 29 streams and rivers in the United States. The training of the ANN model was accomplished with an explained variance of 90% of the dispersion coefficient. The dispersion coefficient values predicted by the ANN model satisfactorily compared with the measured values corresponding to different hydraulic and geometric characteristics. The predicted values were also compared with those predicted using several equations that have been suggested in the literature and it was found that the ANN model was superior in predicting the dispersion coefficient. The results of sensitivity analysis indicated that the Q data alone would be sufficient for predicting more frequently occurring low values of the dispersion coefficient (K-x < 100 m(2)/s). For narrower channels (B/H < 50) using only U/u* data would be sufficient to predict the coefficient. If beta and sigma were used along with the flow variables, the prediction capability of the ANN model would be significantly improved.