Bayesian design of concrete with amortized Gaussian processes and multi-objective optimization

Abstract

Here, we present a computational framework, combining machine learning models with inverse optimization, which can accelerate and optimize concrete mix design with respect to climate impact and/or cost. Our approach leverages a novel amortized Gaussian process (GP) model trained on a large industry dataset to predict concrete strength based on mix proportions. The resulting GP model has an R2 value, RMSE, and MAPE of ∼0.88, ∼909 psi (6.3 MPa), and ∼10.8 %, respectively. We integrated the GP model with an inverse optimization scheme to predict optimal mix designs that minimize cost and/or climate impact. The results show that this integrated framework can generate reasonable concrete mixes that offer up to ∼30 % and ∼60 % reductions in cost and climate impact, respectively, compared with industry mixes with similar 28-day strength. This study highlights the potential environmental and economic benefits of data-driven approaches to designing and optimizing concrete mixes.