The aim of the work reported in this article was to investigate the effects of medium temperature and industrial by-products on the key hardened properties of high performance concrete. Four concrete mixes were prepared based on a water-to-binder ratio of 0.35. Two industrial by-products, silica fume and Class F fly ash, were used separately and together with normal portland cement to produce three concrete mixes in addition to the control mix. The properties of both fresh and hardened concretes were examined in the laboratory. The freshly mixed concrete mixes were tested for slump, slump flow, and V-funnel flow. The hardened concretes were tested for compressive strength and dynamic modulus of elasticity after exposing to 20, 35 and 50 °C. In addition, the initial surface absorption and the rate of moisture movement into the concretes were determined at 20 °C. The performance of the concretes in the fresh state was excellent due to their superior deformability and good segregation resistance. In their hardened state, the highest levels of compressive strength and dynamic modulus of elasticity were produced by silica fume concrete. In addition, silica fume concrete showed the lowest level of initial surface absorption and the lowest rate of moisture movement into the interior of concrete. In comparison, the compressive strength, dynamic modulus of elasticity, initial surface absorption, and moisture movement rate of silica fume-fly ash concrete were close to those of silica fume concrete. Moreover, all concretes provided relatively low compressive strength and dynamic modulus of elasticity when they were exposed to 50 °C. However, the effect of increased temperature was less detrimental for silica fume and silica fume-fly ash concretes in comparison with the control concrete.
Modeling is a very useful method for the performance prediction of concrete. Most of the models available in literature are related to the compressive strength because it is a major mechanical property used in concrete design. Many attempts were taken to develop suitable mathematical models for the prediction of compressive strength of different concretes, but not for self-consolidating high-strength concrete (SCHSC) containing palm oil fuel ash (POFA). The present study has used artificial neural networks (ANN) to predict the compressive strength of SCHSC incorporating POFA. The ANN model has been developed and validated in this research using the mix proportioning and experimental strength data of 20 different SCHSC mixes. Seventy percent (70%) of the data were used to carry out the training of the ANN model. The remaining 30% of the data were used for testing the model. The training of the ANN model was stopped when the root mean square error (RMSE) and the percentage of good patterns was 0.001 and ≈100%, respectively. The predicted compressive strength values obtained from the trained ANN model were much closer to the experimental values of compressive strength. The coefficient of determination (R²) for the relationship between the predicted and experimental compressive strengths was 0.9486, which shows the higher degree of accuracy of the network pattern. Furthermore, the predicted compressive strength was found very close to the experimental compressive strength during the testing process of the ANN model. The absolute and percentage relative errors in the testing process were significantly low with a mean value of 1.74 MPa and 3.13%, respectively, which indicated that the compressive strength of SCHSC including POFA can be efficiently predicted by the ANN.