In our dissertation we dealt with the estimation of the load bearing capacity of reinforced concrete (RC) frame structures after fire, which we carried out in two parts. In the first part we presented various non-destructive and destructive methods for concrete testing used in the experimental part. Within the experimental investigation we prepared five different concrete mixtures with limestone aggregate, which differ in water to cement ratio, type of cement and the amount of water and additives. After the curing and air drying procedure the concrete samples were exposed to high temperatures 200 %C, 400 %C, 600 %C or 800 %C in an electric furnace and then cooled to the room temperature. This was followed by experimental investigation using non-destructive and destructive test methods. The reference values of the experimental measurements were determined on a non-preheated group of test specimens. The results of the non-destructive tests include the determination of the ultrasound (US) pulse velocity, the surface strength, the dynamic modulus of elasticity and the shear modulus of concrete, while destructive tests include the determination of the compressive and flexural strengths and the modulus of elasticity of concrete. Using statistical methods it was then determined that temperature has a statistically significant influence on the above mentioned experimental results, meaning that changes between individual preheated specimens can be detected. This was followed by an estimation of the mechanical properties of concrete after exposure to high temperatures, also named residual mechanical properties, using regression models with explicit relationships and artificial neural networks. We found that the residual flexural strength and modulus of elasticity of concrete can be estimated very accurately based on regression models with explicit relationships, whereas a more accurate estimation of residual compressive strength requires the use of artificial neural networks. In the second part a numerical model for the determination of the fire resistance of planar RC structures after a fire, named Nfira, is briefly presented. The novelty of the numerical model are the experimentally determined material parameters of the constitutive law of limestone concrete after exposure to high temperatures. This was followed by the parametric studies in which the influence of different fire scenarios and concrete mixture on the behavior of planar RC structures after fire were investigated.In our dissertation we dealt with the estimation of the load bearing capacity of reinforced concrete (RC) frame structures after fire, which we carried out in two parts. In the first part we presented various non-destructive and destructive methods for concrete testing used in the experimental part. Within the experimental investigation we prepared five different concrete mixtures with limestone aggregate, which differ in water to cement ratio, type of cement and the amount of water and additives. After the curing and air drying procedure the concrete samples were exposed to high temperatures 200 %C, 400 %C, 600 %C or 800 %C in an electric furnace and then cooled to the room temperature. This was followed by experimental investigation using non-destructive and destructive test methods. The reference values of the experimental measurements were determined on a non-preheated group of test specimens. The results of the non-destructive tests include the determination of the ultrasound (US) pulse velocity, the surface strength, the dynamic modulus of elasticity and the shear modulus of concrete, while destructive tests include the determination of the compressive and flexural strengths and the modulus of elasticity of concrete. Using statistical methods it was then determined that temperature has a statistically significant influence on the above mentioned experimental results, meaning that changes between individual preheated specimens can be detected. This was followed by an estimation of the mechanical properties of concrete after exposure to high temperatures, also named residual mechanical properties, using regression models with explicit relationships and artificial neural networks. We found that the residual flexural strength and modulus of elasticity of concrete can be estimated very accurately based on regression models with explicit relationships, whereas a more accurate estimation of residual compressive strength requires the use of artificial neural networks. In the second part a numerical model for the determination of the fire resistance of planar RC structures after a fire, named Nfira, is briefly presented. The novelty of the numerical model are the experimentally determined material parameters of the constitutive law of limestone concrete after exposure to high temperatures. This was followed by the parametric studies in which the influence of different fire scenarios and concrete mixture on the behavior of planar RC structures after fire were investigated.
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