Civil Engineering / İnşaat Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/13
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Article Citation - WoS: 29Citation - Scopus: 40Geothermal Resources for Sustainable Development: a Case Study(Wiley, 2022) Baba, Alper; Chandrasekharam, DornadulaTurkey's primary energy source is fossil fuels, with a contribution of 55%. According to the International Energy Agency forecast, fossil fuels will continue to be the primary energy source for the next decade. The current CO2 emissions from fossil fuel-based energy are 400 Mt. If the present energy usage trend continues, then the emissions will cross 500 Mt by 2030. However, Turkey has large scope to mitigate climate-related issues and follow sustainable development agenda by increasing the share of geothermal energy as a primary energy source mix. The country established a strong geothermal energy program in 1984 by installing a 17 MWe geothermal power plant in Kızıldere and made tremendous progress in this field. Currently, the power generation has crossed 1665 MWe. Turkey has drawn a new road map to enhance its primary energy source mix by developing its radiogenic granites (Enhanced Geothermal Systems) for power generation and carbon dioxide capture programs. This is an emerging technology that is being recommended for Turkey. Currently, France, Australia, and the United Kingdom are surging ahead in implementing Enhanced Geothermal Systems (EGS), and France has established a pilot power plant using EGS and generating 10 MWe. The United Kingdom will be starting its 3 MWe power plant. The hydrothermal source, in combination with Enhanced Geothermal Systems, can contain the annual CO2 emissions to 500 Mt and reduce the per-capita CO2 emissions to 4.5 tons annually. One of the greatest contributions to climate mitigation and sustainable development made by the geothermal industry is the sequestration of CO2 from the Kızıldere geothermal power plant for the manufacturıng of dry ice and use CO2 from the Tuzla geothermal power plant for minimizing scaling. This dry ice technology can be extended to the cement industry to capture 18 billion CO2 being emitted annually from clinker manufacturıng units. The dry ice will be useful in combating forest fires that are common in Turkey. The article discusses the new technological developments that Turkey is adopting to mitigate climate change and achieve sustainable development goals.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, MustafaThe 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: 5Citation - Scopus: 6Prediction 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, EbruBased 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.