The paper presents the estimation of predicting mechanical properties of concrete after exposure to elevated temperatures using different regression models and the results from non-destructive test techniques. From a set of different non-destructive techniques, the ultrasonic (US) and rebound number techniques were selected, as portthey can be used directly on a reinforced concrete (RC) structure after exposure to fire. Based on known mechanical properties of concrete after exposure to fire the residual load-bearing capacity of the structure can be estimated. The results are based on extensive experimental study that was carried out on concrete specimens of different concrete mixtures that differed in water to cement (w/c) ratio, the type of used cement, and the amount of added superplasticizer. Next, the specimens were exposed to various high temperatures, i.e. 200 °C, 400 °C, 600 °C, and 800 °C. After the specimens had cooled down to the ambient temperature, non-destructive test techniques were performed, followed by destructive ones. The explicit relationships between the results of non-destructive and destructive test techniques, performed on concrete specimens after exposure to high temperatures, were improved by using artificial neural network (ANN) approach. It was established that by using the ANN approach, good estimation of the mechanical properties of concrete after exposure to high temperatures can be made. At the same time, this estimation is good enough only if the results of the US method are used. However, the estimation can be improved by adding information about the concrete mixture or the maximum temperature reached.
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