Civil Engineering / İnşaat Mühendisliği

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

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Now showing 1 - 4 of 4
  • 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.
  • Conference Object
    Upscaling Surface Flow Equations Depending Upon Data Availability at Different Scales
    (Springer Verlag, 2003) Tayfur, Gökmen
    St. Venant equations, which are used to model sheet flows, are point-scale, depth-averaged equations, requiring data on model parameters at a very fine scale. When data are available at the scale of a hillslope transect, the point equations need to be upscaled to conserve the mass and momentum at that scale, Hillslope-scale upscaled model must be developed if data are available at that scale. The performance of the three models applied to simulate flows from non-rilled surfaces revealed that the hillslope-scale upscaled model performs as good as the point-scale model though it uses far less data. The transectionally-upscaled model slightly underestimates the observed data.
  • 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.