Civil Engineering / İnşaat Mühendisliği

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

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  • Article
    Constitutive Equation Determination and Dynamic Numerical Modelling of the Compression Deformation of Concrete
    (Wiley, 2021) Seven, Semih Berk; Çankaya, M. Alper; Uysal, Çetin; Taşdemirci, Alper; Saatci, Selçuk; Güden, Mustafa
    The dynamic compression deformation of an in-house cast concrete (average aggregate size of 2-2.5 mm) was modelled using the finite element (FE), element-free Galerkin (EFG) and smooth particle Galerkin (SPG) methods to determine their capabilities of capturing the dynamic deformation. The numerical results were validated with those of the experimental split Hopkinson pressure bar tests. Both EFG and FE methods overestimated the failure stress and strain values, while the SPG method underestimated the peak stress. SPG showed similar load capacity profile with the experiment. At initial stages of the loading, all methods present similar behaviour. Nonetheless, as the loading continues, the SPG method predicts closer agreement of deformation profile and force histories. The increase in strength at high strain rate was due to both the rate sensitivity and lateral inertia caused by the confinement effect. The inertia effect of the material especially is effective at lower strain values and the strain rate sensitivity of the concrete becomes significant at higher strain values.
  • Article
    Citation - WoS: 5
    Citation - Scopus: 6
    Prediction of Rainfall Runoff-Induced Sediment Load From Bare Land Surfaces by Generalized Regression Neural Network and Empirical Model
    (Wiley, 2020) Tayfur, Gökmen; Aksoy, Hafzullah; Eriş, Ebru
    Based on three rainfall run-off-induced sediment transport data for bare surface experimental plots, the generalized regression neural network (GRNN) and empirical models were developed to predict sediment load. Rainfall intensity, slope, rainfall duration, soil particle median diameter, clay content of the soil, rill density and soil particle mass density constituted the input variables of the models while sediment load was the target output. The GRNN model was trained and tested. The GRNN model was found successful in predicting sediment load. Sensitivity analysis by the GRNN model revealed that slope and rainfall duration were the most sensitive parameters. In addition to the GRNN model, two empirical models were proposed: (1) in the first empirical model, all the input variables were related to the sediment load, and (2) in the second empirical model, only rainfall intensity, slope and rainfall duration were related to the sediment load. The empirical models were calibrated and validated. At the calibration stage, the coefficients and the exponents of the empirical models were obtained using the genetic algorithm optimization method. The validated empirical models were also applied to two more experimental data sets: (1) one data set was from a field experiment, and (2) one set was from a laboratory experiment. The results indicated the success of the empirical models in predicting sediment load from bare land surfaces.