Civil Engineering / İnşaat Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/13
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Article Citation - WoS: 44Citation - Scopus: 47Predicting Flood Plain Inundation for Natural Channels Having No Upstream Gauged Stations(IWA Publishing, 2019) Kaya, C. Melisa; Tayfur, Gökmen; Güngör, OğuzFlow hydrographs are one of the most important key elements for flood modelling. They are recorded as time series; however, they are not available in most developing countries due to lack of gauged stations. This study presents a flood modelling method for rivers having no upstream gauged stations. The modelling procedure involves three steps: (1) predicting upstream hydrograph by the reverse flood routing method which requires information about channel geometric characteristics, downstream flow stage and downstream flow hydrographs; (2) modelling flood wave spreading using HEC-RAS. The hydrograph predicted by the reverse flood routing in the first step becomes an inflow for the HEC-RAS model; (3) delineating the flood-risk areas by overlapping the Geographical Information System (GIS)-based flood maps produced by the HEC-RAS to the related orthophoto images. The developed model is applied to Guneysu Basin in Rize Province in Eastern Black Sea Region of Turkey. The model-produced flood map is compared to the observed one with success.Article Citation - WoS: 33Citation - Scopus: 37Trend Analysis of Temperature and Precipitation in Trarza Region of Mauritania(IWA Publishing, 2019) Yacoub, Ely; Tayfur, GökmenTrend analysis of annual temperature and precipitation time series data collected from three stations (Boutilimit (station 1), Nouakchott (station 2) and Rosso (station 3)) has been used to detect the impacts of climate change on water resources in Trarza region, Mauritania. The Mann-Kendall, the Spearman's rho, and the Sen trend test were used for the trend identification. Pettitt's test was used to detect the change point of the series while the Theil-Sen approach was used to estimate the magnitude of the slope in the series. For precipitation, two stations (1 and 3) indicated statistically significant increase in trends. In the case of temperature, almost all the stations show statistically significant increasing trends in the maximum, minimum, and average temperatures. The magnitude of precipitation detected by the Theil-Sen test for stations 1 and 3, respectively, was found to be at the rate of 2.93 and 3.35 mm/year at 5% significance level. The magnitude trend of temperature detected by the Theil-Sen approach was found to be at the rate of 0.2-0.4 degrees C per decade for almost all the stations. The change points of temperature trends detected by Pettitt test are found to be in the same year (1995) for all the stations.Article Citation - WoS: 8Citation - Scopus: 9Dynamic Behavior Predictions of Fiber-Metal Laminate/Aluminum Foam Sandwiches Under Various Explosive Weights(SAGE Publications, 2016) Baştürk, Suat Bahar; Tanoğlu, Metin; Çankaya, Mehmet Alper; Eğilmez, Oğuz ÖzgürApplication of blast tests causes some problems to characterize the performance of panels due to the drastic conditions of explosive medium. Real test has high safety concerns and is not easily accessible because of its extra budget. Some approaches are needed for the preliminary predictions of dynamic characteristics of panels under blast loading conditions. In this study, the response of sandwiches under blast effect was evaluated by combining quasi-static experiments and computational blast test data. The primary aim is to relate the quasi-static panel analysis to dynamic blast load. Based on this idea, lightweight sandwich composites were subjected to quasi-static compression loading with a special test apparatus and the samples were assumed as single degree-of-freedom mass-spring systems to include dynamic effect. This approach provides a simpler way to simulate the blast loading over the surface of the panels and reveals the possible failure mechanisms without applying any explosives. Therefore the design of the panels can be revised by considering quasi-static test results. In this work, the peak deflections and survivabilities of sandwiches for various explosive weights were predicted based on the formulations reported in the literature. Major failure types were also identified and evaluated with respect to their thicknesses.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: 22Citation - Scopus: 27Micromechanical Modeling of Intrinsic and Specimen Size Effects in Microforming(Springer Verlag, 2018) Yalçınkaya, Tuncay; Özdemir, İzzet; Simonovski, IgorSize effect is a crucial phenomenon in the microforming processes of metallic alloys involving only limited amount of grains. At this scale intrinsic size effect arises due to the size of the grains and the specimen/statistical size effect occurs due to the number of grains where the properties of individual grains become decisive on the mechanical behavior of the material. This paper deals with the micromechanical modeling of the size dependent plastic response of polycrystalline metallic materials at micron scale through a strain gradient crystal plasticity framework. The model is implemented into a Finite Element software as a coupled implicit user element subroutine where the plastic slip and displacement fields are taken as global variables. Uniaxial tensile tests are conducted for microstructures having different number of grains with random orientations in plane strain setting. The influence of the grain size and number on both local and macroscopic behavior of the material is investigated. The attention is focussed on the effect of the grain boundary conditions, deformation rate and the grain size on the mechanical behavior of micron sized specimens. The model is intrinsically capable of capturing both experimentally observed phenomena thanks to the incorporated internal length scale and the crystallographic orientation definition of each grain.Article Citation - WoS: 3Citation - Scopus: 3A Quality Assessment of Public Water Fountains and Relation To Human Health: a Case Study From Yozgat, Turkey(John Wiley and Sons Inc., 2019) İritaş, Servet Birgin; Türksoy, Vugar Ali; Deniz, Serdar; Koçoğlu, Serhat; Kırat, Güllü; Demirkesen, Ali Can; Baba, AlperPublic fountains are very common and everyday people appreciate the benefits a water fountain can bring. However, consumption of public fountain water in some country has decreased because of growing concerns that constituents in fountain water may have adverse effects on health. A few studies have examined the safety of public fountains, proposing only limited evidence of fountain-related health issues in Turkey. Most of these public fountains are sourced from natural springs in Turkey. In this study, a 177 fountain water and 32 rock samples were analysed for source and quality of water. The geology of the region has the direct impact on the quality of the public fountain water. The results indicate that the level of some elements exceeded the limit values determined by WHO and US.EPA. The most striking high values were observed for iron (Fe), nickel (Ni), aluminum (Al), arsenic (As) and bromine (Br) concentrations.Article Citation - WoS: 2Citation - Scopus: 4Removal of Metals and Metalloids From Acidic Mining Lake (aml) Using Olive Oil Solid Waste (osw)(Springer Verlag, 2019) İlay, Remzi; Baba, Alper; Kavdır, YaseminThe acidic mining lakes have low pH values and high metal and metalloid concentrations. In this study, the ability of low-cost olive oil solid waste (OSW) to remove Al, As, Cd, Fe, B and Ti ions from aqueous solutions in short term has been evaluated. Adsorption capacities (mg g−1) of OSW (1:5–1:10 w/v) were 764.06–411.75 for Al, 0.26 for As, 0.07–0.14 for Cd, 2181.5–2406.5 for Fe, 23.70–82.50 for B and 0.12–0.0.34 for Ti. OSW addition increased acidic mine water (AMW) pH from 2.41 to 3.2 with 1:5 and from 2.41 to 2.7 to 1:10 mixing ratio, respectively, after 10 min. The best gradual decrease has been observed with different ratio of OSW applications on B and Ti concentrations. OSW adsorbs 32.41% and 62.68% of B at the ratio of 1:5 and 1:10 and 55.29% and 83.04% of Ti at the ratio of 1:5 and 1:10 (OSW:AMW) mixtures, respectively. The results show that OSW has great potential for metal removal from acidic mine water.Article Citation - WoS: 54Citation - Scopus: 65Flood Hydrograph Prediction Using Machine Learning Methods(MDPI Multidisciplinary Digital Publishing Institute, 2018) Tayfur, Gökmen; Singh, Vijay P.; Moramarco, Tommaso; Barbetta, SilviaMachine learning (soft) methods have a wide range of applications in many disciplines, including hydrology. The first application of these methods in hydrology started in the 1990s and have since been extensively employed. Flood hydrograph prediction is important in hydrology and is generally done using linear or nonlinear Muskingum (NLM) methods or the numerical solutions of St. Venant (SV) flow equations or their simplified forms. However, soft computing methods are also utilized. This study discusses the application of the artificial neural network (ANN), the genetic algorithm (GA), the ant colony optimization (ACO), and the particle swarm optimization (PSO) methods for flood hydrograph predictions. Flow field data recorded on an equipped reach of Tiber River, central Italy, are used for training the ANN and to find the optimal values of the parameters of the rating curve method (RCM) by the GA, ACO, and PSO methods. Real hydrographs are satisfactorily predicted by the methods with an error in peak discharge and time to peak not exceeding, on average, 4% and 1%, respectively. In addition, the parameters of the Nonlinear Muskingum Model (NMM) are optimized by the same methods for flood routing in an artificial channel. Flood hydrographs generated by the NMM are compared against those obtained by the numerical solutions of the St. Venant equations. Results reveal that the machine learning models (ANN, GA, ACO, and PSO) are powerful tools and can be gainfully employed for flood hydrograph prediction. They use less and easily measurable data and have no significant parameter estimation problem.Article Citation - WoS: 1Citation - Scopus: 1Finite Volume Method Solution of Pollutant Transport in Catchment Sheet Flow(IWA Publishing, 2014) Tayfur, Gökmen; He, Zhiguo; Ran, QihuaA finite volume numerical method was employed in the solution of two-dimensional pollutant transport in catchment sheet flow. The full dynamic wave constituted the sheet flow while the advection-diffusion equation with sink/source terms was the pollutant transport model. It is assumed that the solute in the surface active layer is uniformly distributed and the exchange rate of the solute between the active layer and overland flow are proportional to the difference between the concentrations in soil and water volume. Decrease of the solute transfer rate in the active surface layer caused by the transfer of solutes from soil to the overlying runoff is assumed to follow an exponential law. The equations governing sheet flow and pollutant transport are discretized using the finite volume method in space and an implicit backward difference scheme in time. The model was subjected to several numerical tests involving varying microtopographic surface, roughness, and infiltration. The results revealed that spatially varying microtopography plays an important role unlike the roughness and infiltration with respect to the total pollutant rate from the outlet of a catchment.Article Citation - WoS: 4Citation - Scopus: 5Use of Principal Component Analysis in Conjunction With Soft Computing Methods for Investigating Total Sediment Load Transferability From Laboratory To Field Scale(IWA Publishing, 2014) Tayfur, Gökmen; Karimi, YasharThis study quantitatively investigates the generalization from laboratory scale to field scale using the soft computing (expert) and the empirical methods. Principal component analysis is utilized to form the input vector for the expert methods. Five main dimensionless parameters are used in the input vector of artificial neural networks (ANN), calibrated with laboratory data, to predict field total sediment loads. In addition, nonlinear equations are constructed based upon the same dimensionless parameters. The optimal values of the exponents and constants of the equations are obtained by the genetic algorithm (GA) method using the laboratory data. The performance of the sodeveloped ANN and GA based models are compared against the field data and those of the existing empirical methods, namely Bagnold, Ackers and White, and Van Rijn. The results show that ANN outperforms the empirical methods. The results also show that the expert models, calibrated with laboratory data, are capable of predicting field total loads and thus proving their transferability capability. The transferability is also investigated by a newly proposed equation which is based on the Bagnold approach. The optimal values of the coefficients of this equation are obtained by the GA. The performance of the proposed equation is found to be very efficient.