Resumen
In this study, an artificial neural network (ANN) model was developed to predict the compressive strength of concrete containing supplementary cementitious materials (SCMs) at high temperatures. For this purpose, 500 experimental results were collected from the available literature. The effective parameters in the model are the volumes of coarse and fine aggregates, water, cement, coarse-aggregate type, percentage SCMs as the cement replacement, temperature levels, and test methods. The proposed ANN model was developed at a correlation coefficient of 0.966. A parametric study was conducted to evaluate the impact of the combined effects of input parameters (aggregate types and SCM content) on the relative compressive strength of concrete at high temperatures. It was shown that siliceous aggregate has a better performance by producing stronger bonds with cement paste than calcareous aggregates. The optimum SCM contents depend on the aggregate types. The optimum silica fume (SF) content for concrete with a water-to-binder ratio of 0.6 subjected to high temperatures is 8% and 3% for siliceous and calcareous concrete, respectively. The analysis of the ANN model has provided a conclusive understanding of the concrete behaviour at high temperatures.