In this paper the effect of carbonation on chloride diffusivity in concrete is investigated. An artificial neural network model was used to determine the relation between chloride diffusion coefficients and concrete mix design in carbonated and non-carbonated concretes. The models were trained by results of chloride profile experiments. Input parameters were water-to-binder ratios, the amount of silica fume, rapid chloride ion permeability test and capillary absorption coefficient. The output parameter was chloride diffusion coefficient. The neural network models are multi-layer perceptron models and they differ in the number of hidden layers and neurons.
In the past few years, the use of carbon fibre reinforced polymer (CFRP) has increased, because of their high strength, in concrete elements such as shear walls. In this study, the behaviour of a shear wall strengthened with different layout configurations of CFRP under lateral loading was investigated. For this purpose, a model is first verified in laboratory work, then in the next step the models were analysed by increasing the number of fibre layers and the effect of fibre layers on shear wall capacity was studied. Sliding between fibres and concrete was neglected. Also the effect of increasing the concrete strength of a reinforced concrete (RC) wall on CFRP strengthening was studied. In all models, comparisons were made between the results of CFRP configurations in increasing lateral strength and also ductility. Finally, by comparing the results, the best fibre configuration was determined based on the maximum load capacity.
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