Recently, self-compacting concrete (SCC) has been increasingly used in high-rise buildings and industrial units, susceptible to accidental fires. The probable degradation of these structures necessitates understanding SCC behavior under elevated temperatures. For this, an extensive experimental investigation was undertaken to evaluate the effect of elevated temperature (300–600 °C) on the mechanical compressive properties of SCC; considering the effect of water-to-cement ratio (0.40–0.50), type of mineral aggregate and filler (limestone and basalt), and internal humidity. Standard cylinder (150 mm × 300 mm) and prism (100 mm × 100 mm × 300 mm) specimens were prepared from various SCC mixtures, cured for 28 d in limewater, and then stored at different environments for an additional 90 d to create varying internal humidity levels; ranging from 28 to 95%. Later, specimens were subjected to elevated temperatures in an electrical furnace, then cooled and tested for compressive mechanical response or non-destructively using resonance frequency, ultrasonic pulse velocity, and rebound hammer evaluation techniques. The results showed significant reduction in residual compressive strength, and elastic modulus, and an increase in compressive strain at peak stress and toughness as elevated temperature was increased. The SCC mixtures at upper water-to-cement ratios with basalt aggregate showed higher resistance to elevated temperatures than corresponding ones with limestone. Internal humidity in SCC had a detrimental impact on compressive strength and elastic modulus; especially at exposure temperatures below 400 °C. The statistical correlations between residuals for compressive strength or elastic modulus and nondestructive damage indices can be classified as very good. Furthermore, the nonlinear empirical models, developed to predict residuals for compressive strength and elastic modulus in terms of the study parameters, showed relatively high prediction potential, hence are recommended to be used in designing SCC mixtures for best resistance against possible fire attack.
Prediction of self-compacting concrete elastic modulus using two symbolic regression techniques
