Data-driven mechanical property prediction and mix design optimization of recycled brick aggregate concrete

Using recycled brick aggregates (RBAs) in concrete provides an eco-friendly and cost-effective alternative to natural aggregates. However, higher RBA replacement ratios can compromise mechanical performance, posing a challenge to sustainability without sacrificing structural integrity. To tackle this, a multi-objective mix design approach is necessary. This study thus introduces an intelligent optimization framework for recycled brick aggregate concrete (RBAC), which first develops predictive models for compressive strength ( f c ) and elastic modulus ( E c ) using Markov Chain Monte Carlo sampling, based on 703 experimental data points. A life cycle assessment (LCA) module then evaluates production costs and carbon emissions, considering the effects of RBA replacement ratio and water absorption, while maintaining mechanical performance. To find optimal mix designs, a cuckoo search algorithm balances mechanical, economic and environmental goals. The results show: (i) The predictive models capture the influence of key variables and outperform traditional empirical equations in accuracy. (ii) When optimizing for f c , E c and cost, the framework achieves up to 36% cost savings; for f c , E c and carbon emissions, reductions reach 49%. A combined optimization results in 25% cost savings and 38% emissions reduction. (iii) RBAs are unsuitable for concrete with compressive strength above 50 MPa but work well for 20 MPa mixes, with water absorption limited to 9.4% for strengths ≥30 MPa. (iv) Overall, the framework enhances the effective utilization of RBAs and offers practical strategies for sustainable and performance-oriented concrete mix design.