Active learning-based Bayesian optimization in the realm of copper slag-blended cement systems

Abstract

Accelerated mix design optimization is critical for deploying low-carbon supplementary cementitious materials (SCMs) because traditional experimental approaches require extensive testing campaigns. The authors demonstrate that Bayesian optimization (BO) can identify near-optimal blended cement formulations using an AI-driven approach with minimal experimental data. Starting with only 10 initial experiments and a 2:1 data-to-variable ratio reflecting realistic laboratory constraints, the authors optimized copper slag, limestone, Portland cement systems for 32.5N strength class with respect to 2-day compressive strength, cost, and CO2 emissions. Gaussian process surrogate models guided sequential experimentation through Expected Improvement and Upper Confidence Bound acquisition functions. Within 2-6 iterations, BO identified Pareto non-dominated solutions meeting strength requirements (greater than 10 MPa at 2 days, greater than 32.5 MPa at 28 days) while achieving CO2 emissions below 500 kg CO2/ton; consistent with industry decarbonization targets. A conservative update strategy incorporating uncertainty bounds for 28-day strength enabled rapid iteration without waiting 28 days per cycle. Comparative analysis revealed that linear kernels outperformed nonlinear alternatives in predictive accuracy, though radial basis function kernels were preferred for active learning due to superior uncertainty quantification. This work demonstrates BO as a practical decision-support tool for cement research under severe data constraints.