Proportioning and curing optimization of carbonated calcium silicate-based materials via machine learning and genetic algorithm
Year: 2025
Type: conference-paper
Abstract: Calcium silicate can react with CO2 in the presence of water to gain strength and thus can be both taken as an alternative binder and a carbon sink. A series of calcium silicates exists in industrial products and natural rocks with diverse material properties, implying its promising sustainable potential and yet foreseeable obstacles in quality control. To tackle that, a data-driven prediction-optimization computational framework was proposed based on a high-fidelity dataset of carbonated calcium silicate-based materials. Ensemble machine learning models were built based on the curated data for CO2 uptake and strength predictions. Pareto front searching was further conducted using a genetic algorithm to pursue better sustainability, strength, and curing efficiency of the materials. Reasonable decisions can be made once the trade-offs are considered among the optimal solutions.
Authors: Kangyi Cai, Hongyan Ma
Institution: Missouri University of Science and Technology
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W7084123236
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