Predicting compressive strength of concrete at elevated temperatures and optimizing its mixture proportions

Predicting concrete behavior under high temperatures and optimizing fire-resistant mix designs remain key challenges in civil engineering. To address the issues to a certain extent, this paper integrates Bayesian prediction with Cuckoo search optimization at material scale. A database of 822 high-temperature compressive strength tests was used and Bayesian model updating developed a predictive equation considering factors like water-to-binder ratio (0.27–0.90), fly ash replacement (0–0.55), slag content (0–0.61), aggregate-to-binder ratio (2.64–9.85) and fire temperature (24–700 °C). The Cuckoo search algorithm was then employed to optimize mix designs, balancing high-temperature strength, cost and sustainability. Key findings include: (i) The multiplicative form of the Bayesian model demonstrates high prediction accuracy (R²=0.805), with clear physical interpretation and concise expression form. (ii) At ambient temperature, the incorporation of fly ash and slag significantly mitigates concrete production costs and carbon emissions; nevertheless, these reductions are less substantial than those realized through an increased aggregate-to-binder ratio. (iii) An elevated aggregate-to-binder ratio and increased slag content improve concrete's high-temperature mechanical properties and sustainability, with optimal slag replacement and aggregate-to-binder ratio approximately 40 % and 3.6, respectively. (iv) The integrated mix design, which achieves a reduction of up to 40 % in carbon emissions and 30 % in costs, while preserving mechanical strength, comes highly recommended.