Civil Engineering / İnşaat Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/13
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Article Citation - WoS: 29Citation - Scopus: 32Plate Loading Tests on Clay With Construction and Demolition Materials(Springer Verlag, 2021) Cabalar, Ali Fırat; Abdulnafaa, Mohammed Dafer; İşbuğa, VolkanThis study presents a series results of plate loading tests on a clay with various construction and demolition (CD) materials conducted in a large-scale model box and a numerical verification on the use of these material mixtures. The tests have been applied to the clay with three different types of CD materials (concrete, asphalt, and brick) prepared in a reinforced concrete circular box with a diameter of 2.0 m and a depth of 1.5 m. The CD materials were added to the clay with a mix ratio of 10% by dry weight and then compacted at optimum water content (w(opt)) and corresponding maximum dry density (gamma(drymax)). The testing results have indicated that the CD materials increased the ultimate bearing capacity of the clay with a range of 50-75%. Furthermore, a remarkable correlation between the results of plate loading tests and numerical simulations made by a commercial finite element software (Plaxis 2D) was observed for all mixtures tested.Article Citation - WoS: 1Citation - Scopus: 1Generalized Regression Neural Network and Empirical Models To Predict the Strength of Gypsum Pastes Containing Fly Ash and Blast Furnace Slag(Springer Verlag, 2020) Erdem, Tahir Kemal; Cengiz, Okan; Tayfur, GökmenGypsum is widely used in constructions owing to its easy application, zero shrinkage, and excellent fire resistance. Several parameters can affect the properties of gypsum pastes. To study the strength of the gypsum pastes experimentally by trying all these parameters is time-consuming and costly. Therefore, artificial intelligence methods can be very useful to predict the paste strength, which, in turn, can reduce the number of trial batches. Based on experimental data, the generalized regression neural network (GRNN) and empirical models were developed to predict strength of gypsum pastes containing fly ash (FA) and blast furnace slag (BFS). Gypsum content, pozzolan content, curing temperature, curing duration, and testing age constituted the input variables of the models while the paste strength was the target output. The trained and tested GRNN model was found to be successful in predicting strength. Sensitivity analysis by the GRNN model revealed that the curing duration and temperature were important sensitive parameters. In addition to the GRNN model, empirical models were proposed for the strength prediction. The same input variables formed the input vectors of the empirical models. The same dataset used for the calibration of the GRNN model was employed to establish the empirical models by employing genetic algorithm (GA) method. The empirical models were successfully validated. The GRNN and GA_based empirical models were also tested against the multi-linear regression (MLR) and multi-nonlinear regression (MNLR) models. The results showed the outperformance of the GRNN and the GA_based empirical models over the others.Article Citation - WoS: 18Citation - Scopus: 22The Health Risk Associated With Chronic Diseases in Villages With High Arsenic Levels in Drinking Water Supplies(Springer Verlag, 2017) Gündüz, Orhan; Bakar, Coşkun; Şimşek, Celalettin; Baba, Alper; Elçi, Alper; Gürleyük, Hakan; Mutlu, Merdiye; Çakır, AyşeThis study is intended to compare and assess the distribution and possible causes of current chronic diseases in villages with high arsenic levels in drinking water supplies. It is a cross-sectional epidemiological research that analyzes the frequency and underlying risk factors of chronic diseases in villages with varying levels of arsenic exposure through drinking water. Sample space of study included 1003 individuals, 614 of whom were from villages with high arsenic levels in drinking water and remaining 389 were from two control villages with below-limit arsenic levels in drinking water. While nutritional habits and living environments of two groups were similar, cigarette smoking and alcohol use were higher in villages with low arsenic levels. Mini mental state examination test results in 60+ age group were lower in villages with high arsenic levels. Although no statistically significant differences were detected in chronic disease occurrence between the groups, the number of cases was higher in villages with higher percentage of cigarette smoking and alcohol use. Moreover, cases of lung, colon, and stomach cancers were higher in villages with high arsenic levels in drinking water supplies.Article Citation - WoS: 17Citation - Scopus: 21The Use of Neural Networks for the Prediction of Cone Penetration Resistance of Silty Sands(Springer Verlag, 2017) Erzin, Yusuf; Ecemiş, NurhanIn this study, an artificial neural network (ANN) model was developed to predict the cone penetration resistance of silty sands. To achieve this, the data sets reported by Ecemis and Karaman, including the results of three high-quality field tests, namely piezocone penetration test, pore pressure dissipation tests, and direct push permeability tests performed at 20 different locations on the northern coast of the Izmir Gulf in Turkey, have been used in the development of the ANN model. The ANN model consisted of three input parameters (relative density, fines content, and horizontal coefficient of consolidation) and a single output parameter (normalized cone penetration resistance). The results obtained from the ANN model were compared with those obtained from the field tests. It is found that the ANN model is efficient in determining the cone penetration resistance of silty sands and yields cone penetration resistance values that are very close to those obtained from the field tests. Additionally, several performance indices such as the determination coefficient, variance account for, mean absolute error, root mean square error, and scaled percent error were computed to examine the performance of the ANN model developed. The performance level attained in the ANN model shows that the ANN model developed in this study can be employed for predicting cone penetration of silty sands quite efficiently.Article Citation - WoS: 66Citation - Scopus: 75Modern Optimization Methods in Water Resources Planning, Engineering and Management(Springer Verlag, 2017) Tayfur, GökmenMathematical (analytical, numerical and optimization) models are employed in many disciplines including the water resources planning, engineering and management. These models can vary from a simple black-box model to a sophisticated distributed physics-based model. Recently, development and employment of modern optimization methods (MOMs) have become popular in the area of mathematical modeling. This paper overviews the MOMs based on the evolutionary search which were developed over mostly the last 30 years. These methods have wide application in practice from finance to engineering and this paper focuses mostly on the applications in the area of water resources planning, engineering and management. Although there are numerous optimization algorithms, the paper outlines the ones that have been widely employed especially in the last three decades; such as the Genetic Algorithm (GA), Ant Colony (AC), Differential Evolution (DE), Particle Swarm (PS), Harmony Search (HS), Genetic Programming (GP), and Gene Expression Programming (GEP). The paper briefly introduces theoretical background of each algorithm and its applications and discusses the merits and, if any, shortcomings. The wide spectrum of applications include, but not limited to, flood control and mitigation, reservoir operation, irrigation, flood routing, river training, flow velocity, rainfall-runoff processes, sediment transport, groundwater management, water quality, hydropower, dispersion, and aquifers.Article Citation - WoS: 45Citation - Scopus: 51Evaluation and Assessment of Meteorological Drought by Different Methods in Trarza Region, Mauritania(Springer Verlag, 2017) Yacoub, Ely; Tayfur, GökmenDrought Indexes (DIs) are commonly used for assessing the effect of drought such as the duration and severity. In this study, long term precipitation records (monthly recorded for 44 years) in three stations (Boutilimit (station 1), Nouakchott (station 2), and Rosso (station 3)) are employed to investigate the drought characteristics in Trarza region in Mauritania. Six DI methods, namely normal Standardized Precipitation Index (normal-SPI), log normal Standardized Precipitation Index (log-SPI), Standardized Precipitation Index using Gamma distribution (Gamma-SPI), Percent of Normal (PN), the China-Z index (CZI), and Deciles are used for this purpose. The DI methods are based on 1-, 3-, 6-, and 12 month time periods. The results showed that DIs produce almost the same results for the Trarza region. The droughts are detected in the seventies and eighties more than the 1990s. Twelve drought years might be experienced in station 2 and six in stations 1 and 3 in every 44 years, according to reoccurrence probability of the gamma-SPI and log-SPI results. Stations 1 and 3 might experience fewer drought years than station 2, which is located right on the coast. In station 1, which is located inland, when the annual rainfall is less than 123 mm, it is likely that severe drought would occur. This is 63 mm/year for station 2 and 205 mm/year for station 3 which is located in the south west on the Senegal River. DI results indicate that the CZI and the gamma-SPI methods make similar predictions and the log-SPI makes extreme drought predictions for the monthly period for all the stations. For longer periods (3-, 6-, and 12 month period), for all the stations, the log-SPI and the gamma-SPI produce similar results, making severe drought predictions while the normal-SPI and the CZI methods predict more wet and fewer drought cases. The log-SPI, the gamma-SPI, PN and Deciles were able to capture the historical extreme and severe droughts observed in early 1970s and early 1980s.Article Citation - WoS: 40Citation - Scopus: 50Analysis and Assessment of Hydrochemical Characteristics of Maragheh-Bonab Plain Aquifer, Northwest of Iran(Springer Verlag, 2017) Fijani, Elham; Moghaddam, Asghar A.; Tsai, Frank T.-C.; Tayfur, GökmenThe present study aims at assessing the hydrochemistry of the groundwater system of the Maragheh-Bonab Plain located in the East Azarbaijan Province, northwest of Iran. The groundwater is used mainly for drinking, agriculture and industry. The study also discusses the issue of the industrial untreated wastewater discharge to the Plain aquifer that is a high Ca-Cl water type with TDS value of about 150 g/L. The hydrogeochemical study is conducted by collecting and analyzing the groundwater samples from July and September of 2013. The studied system contains three major groundwater types, namely Ca–Mg–HCO3, Na–Cl, and non-dominant water, based on the analysis of the major ions. The main processes contributing to chemical compositions in the groundwater are the dissolution along the flow path, dedolomitisation, ion exchange reactions, and the mixing with wastewater. According to the computed water quality index (WQI) ranging from 25.45 to 194.35, the groundwater in the plain can be categorized into “excellent water”, “good water”, and “poor water”. There is a resemblance between the spatial distribution of the WQI and hydrochemical water types in the Piper diagram. The “excellent” quality water broadly coincides with the Ca-Mg-HCO3 water type. The “poor” water matches with the Na–Cl water type, and the “good” quality water coincides with blended water. The results indicate that this aquifer suffers from intense human activities which are forcing the aquifer into a critical condition.Article Citation - WoS: 4Citation - Scopus: 8Investigating a Suitable Empirical Model and Performing Regional Analysis for the Suspended Sediment Load Prediction in Major Rivers of the Aegean Region, Turkey(Springer Verlag, 2017) Ülke, Aslı; Tayfur, Gökmen; Özkul, SevinçThis study investigates the appropriateness of four major empirical methods [Lane and Kalinske, Einstein, Brooks, Chang—Simons—Richardson] for predicting suspended sediment loads (SSLs) in three major rivers in the Aegean Region, Turkey. The measured data from 1975 to 2005 were used to test performance of the models. It was found that Brooks method was more appropriate, among the others, for predicting suspended sediment loads from each river. The prediction results of Brooks method were further improved by the use of genetic algorithm (GA_Brooks) optimizing a fitting parameter and showing a comparable performance to those of artificial neural networks (ANNs) and neuro-fuzzy (ANFIS) models for the same rivers. GA_Brooks, ANNs, and ANFIS models can be used for predicting loads at a regional scale. The sensitivity analysis results revealed that suspended and bed material particle diameters affect suspended sediment loads significantly.Article Citation - WoS: 76Citation - Scopus: 84Two-Dimensional Numerical Modeling of Flood Wave Propagation in an Urban Area Due To Ürkmez Dam-Break, Izmir, Turkey(Springer Verlag, 2016) Haltas, İsmail; Tayfur, Gökmen; Elçi, ŞebnemThis study investigated flood inundation in an urban area due to a possible failure of Ürkmez Dam in İzmir, Turkey. The estimation of flood hydrograph upon partial failure of the dam and routing of the flood hydrograph along the narrow valley downstream were first performed by the one-dimensional hydraulic routing model HEC-RAS. The two-dimensional hydraulic routing model FLO-2D is then used to simulate the spreading of the dam-break flood after the flood wave exits the valley. Land use and land cover digital maps were utilized to find the spatially varying roughness coefficient for the floodplain. The influence of the buildings on the flood propagation was represented in the numerical model by the area reduction factor as well as the width reduction factor. The peak flow depth, peak flow velocity and time moment of the peak flow depth maps were shown in the GIS environment. The results reveal that flow depths can reach about 3 m in the residential area. In about 40 min after the dam-break, houses in the large section of the town would be under the maximum flow depths. The two-dimensional hydrodynamic model results were tested against experimental dam-break flow data of the distorted physical model of Ürkmez Dam, which is consisted of the reservoir, dam body and downstream area including Ürkmez Town. The model successfully simulated experimental flow depth data measured at different measurement locations.Article Citation - WoS: 25Citation - Scopus: 26Generation of Acid Mine Lakes Associated With Abandoned Coal Mines in Northwest Turkey(Springer Verlag, 2016) Şanlıyüksel Yücel, Deniz; Balcı, Nurgül; Baba, AlperA total of five acid mine lakes (AMLs) located in northwest Turkey were investigated using combined isotope, molecular, and geochemical techniques to identify geochemical processes controlling and promoting acid formation. All of the investigated lakes showed typical characteristics of an AML with low pH (2.59-3.79) and high electrical conductivity values (1040-6430 μS/cm), in addition to high sulfate (594-5370 mg/l) and metal (aluminum [Al], iron [Fe], manganese [Mn], nickel [Ni], and zinc [Zn]) concentrations. Geochemical and isotope results showed that the acid-generation mechanism and source of sulfate in the lakes can change and depends on the age of the lakes. In the relatively older lakes (AMLs 1 through 3), biogeochemical Fe cycles seem to be the dominant process controlling metal concentration and pH of the water unlike in the younger lakes (AMLs 4 and 5). Bacterial species determined in an older lake (AML 2) indicate that biological oxidation and reduction of Fe and S are the dominant processes in the lakes. Furthermore, O and S isotopes of sulfate indicate that sulfate in the older mine lakes may be a product of much more complex oxidation/dissolution reactions. However, the major source of sulfate in the younger mine lakes is in situ pyrite oxidation catalyzed by Fe(III) produced by way of oxidation of Fe(II). Consistent with this, insignificant fractionation between δ34SSO4 and δ34 SFeS2 values indicated that the oxidation of pyrite, along with dissolution and precipitation reactions of Fe(III) minerals, is the main reason for acid formation in the region. Overall, the results showed that acid generation during early stage formation of an AML associated with pyrite-rich mine waste is primarily controlled by the oxidation of pyrite with Fe cycles becoming the dominant processes regulating pH and metal cycles in the later stages of mine lake development.
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