Optimizing Aggregate Systems Based on a Binary Paste–Aggregate Model

TL;DR

This paper introduces a physics-based method to optimize concrete mix design by minimizing paste volume while maintaining workability and strength.

Contribution

A novel binary paste–aggregate model is proposed, incorporating inter-particle interactions and shape coefficients for improved aggregate packing prediction.

Findings

Inter-particle spacing is identified as a critical factor in controlling concrete flowability.

The model accurately predicts paste demand while maintaining compressive strength and workability.

The approach offers a sustainable alternative to data-driven methods for concrete mix design.

Abstract

This study presents a systematic method for mix design for optimizing the aggregate proportions in concrete, aiming to minimize paste volume while ensuring adequate workability. Based on a binary paste–aggregate system model, the method refines the calculation of the aggregate packing density by excluding fine particles smaller than 75 μm and incorporating inter-particle interactions across multiple size fractions. A modified approach for calculating the aggregate’s specific surface area is introduced, which accounts for both intra-fraction particle size variation and particle morphology through image-based shape coefficients. Inter-particle spacing is identified as a key control parameter of concrete flowability. Using this criterion, an optimization strategy is developed to determine the ideal aggregate composition that achieves the required spacing with the least amount of paste. Experimental validation confirms that the model reliably predicts paste demand while maintaining desired workability and compressive strength. This physics-based, interpretable approach offers a practical alternative to data-intensive machine learning models and contributes to more sustainable and efficient concrete mix design.

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