Civil Engineering / İnşaat Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/13

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Start date: 2020Publisher: search.filters.publisher.Springer Verlag

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  • Article
    Citation - WoS: 29
    Citation - Scopus: 32
    Plate Loading Tests on Clay With Construction and Demolition Materials
    (Springer Verlag, 2021) Cabalar, Ali Fırat; Abdulnafaa, Mohammed Dafer; İşbuğa, Volkan
    This 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: 8
    Citation - Scopus: 8
    Estimation Groundwater Total Recharge and Discharge Using Gis-Integrated Water Level Fluctuation Method: a Case Study From the Alasehir Alluvial Aquifer Western Anatolia, Turkey
    (Springer Verlag, 2020) Şimşek, Celalettin; Demirkesen, Ali Can; Baba, Alper; Kumanlıoğlu, Ahmet; Durukan, Seda; Aksoy, Niyazi; Tayfur, Gökmen
    The estimation of groundwater recharge is an essential process for hydrogeological study. Realistic determination approach is crucial for assessing groundwater potential in an aquifer system and estimating of groundwater levels and/or changes in dry periods. Based on these matters, we employ a GIS-integrated groundwater level fluctuation method to determine the groundwater recharge for a hydrological period in the Alasehir alluvial aquifer (W. Anatolia). The method basically takes into account both increasing and decreasing of the groundwater levels due to the recharge and discharge mechanisms in the aquifer. In this study, 16 pumping and monitoring wells were drilled with a total depth of 1300 m, and water level data loggers were installed into the monitoring wells to determine the groundwater level changes. The spatial distribution of the monthly groundwater level change map was multiplied by the aquifer storage distribution map and then the accurate water volume is calculated by using the 3-D spatial analysis. According to our evaluation in the aquifer, positive volume change of the groundwater is 187 hm(3) in a year, which is considered as a recharge value of groundwater. It is concluded that the GIS-integrated water table fluctuation method gave rise to estimate the total recharge amount of the groundwater in the Alasehir aquifer. The total groundwater recharge indicates that total inflow in the aquifer from precipitation, leakage from surface water and irrigation waters. It can be stated that the recharge estimation of groundwater in a surficial aquifer, like the Alasehir aquifer, is fairly easy using the GIS-integrated water table fluctuation method.
  • Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Generalized 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, Gökmen
    Gypsum 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: 4
    Citation - Scopus: 5
    Pre-Identification Data Merging for Multiple Setup Measurements With Roving References
    (Springer Verlag, 2020) Ceylan, Hasan; Turan, Gürsoy; Hızal, Çağlayan
    One-time operational modal analysis (OMA) of large civil structures requires measurements of the vibrations, which, according to the number of channels to be measured, are generally expensive and arduous to obtain. In this study, identification of modal parameters of civil structures has been investigated by using multiple setups with a roving reference channel. In this manner, a limited amount of equipment becomes sufficient for OMA of structures. The procedure consists of a transformation function between measurement setups, which transforms all measured data to the time frame of a selected reference setup. To illustrate the procedure, an existing 10 story laboratory shear frame model is considered. A numerical and an experimental investigation have been carried out to identify its modal characteristics. The validity of the procedure has been explained in detail by making use of a coherence function in-between the multi-setup measurements. According to the results, OMA by using only a few sensors with the performed procedure can be equivalent to OMA by using a full measurement setup. Against a common believe, the results of this study reveal that synchronization among the setups does not prominently affect the identification results.