Title:
Concrete Mix Design Optimization: Leveraging Machine Learning and Bayesian Optimization for Developing Low-CO2 Cost-Efficient Mixtures Containing SCM
Author(s):
Akbar
Publication:
Web Session
Volume:
ws_S25_Akbar.pdf
Issue:
Appears on pages(s):
Keywords:
DOI:
Date:
3/30/2025
Abstract:
Advancements in AI and computational models have significantly enhanced the predictability of concrete performance by leveraging extensive datasets. Recently, machine learning models have been developed to predict concrete’s compressive strength based on its mixture proportions. However, these models treat supplementary cementitious materials (SCMs) as a categorical (as opposed to quantitative) parameter and do not account for the significant impact of the SCM reactivity on concrete’s strength development. In this study, we assembled a dataset of binary (cement-SCM) mixtures, incorporating SCM reactivity measured by the R3 (ASTM C1897) test. Utilizing a random forest machine learning model, we demonstrated that integrating SCM reactivity significantly enhances the model's predictive performance with the fewest input parameters (w/cm, SCM/cm, SCM R3 heat, Agg/cm, cement CaO%). Further, we implemented a multi-objective Bayesian optimization framework to assist in the mixture proportioning of low-carbon low-cost concrete utilizing cement(s) and SCM(s) available to a concrete producer. This framework proposes concrete mix designs to meet a target 28-day compressive strength while minimizing cost and CO2 emissions, by leveraging SCMs with varied reactivity levels. The proposed mix designs were further validated with experiments. The work demonstrates how to avoid model extrapolation and erroneous predictions by utilizing a multi-dimensional convex envelop algorithm. Overall, the outcomes of this work provide a valuable tool for the concrete industry which can be expanded to predict and incorporate other metrics of concrete performance (e.g., workability, durability) and develop optimized mix designs accordingly.