journal article·2025-12Open access

An optimized procedure for cleaner concrete production with reduced CO2 emissions

Ángel De La Rosa, Rena C. Yu, Gonzalo Ruiz

Abstract

This paper presents a novel mix design methodology for the sustainable formulation of self-compacting concrete (SCC) and self-compacting steel fiber-reinforced concrete (SCSFRC), incorporating both Portland cement and natural hydraulic lime as binders. The approach integrates environmental and economic criteria within a two-stage optimization framework. First, feasible mix compositions are generated using a validated rheological–mechanical model. Then, a nonlinear programming algorithm, implemented in GAMS, identifies optimal proportions by minimizing a total cost function that includes both raw material prices and the environmental cost of CO 2 emissions, expressed in European Allowance units. The methodology is applied to nine concrete mixtures with compressive strengths ranging from 3.5 MPa to 70 MPa. Results show that CO 2 emissions can be reduced by up to 4.2%, with typical reductions between 2.6% and 3.6%, depending on the formulation. Comparative analysis confirms that concrete based on CEM II 32.5 achieves lower emissions than CEM I at equivalent strength levels, while high-strength applications still require CEM I despite its higher environmental impact. Lime-based concretes offer moderate emissions at low strength, but emissions rise substantially at higher strengths due to increased binder demand. These findings validate the proposed methodology as a robust tool for optimizing concrete mix design, enabling the selection of formulations that meet mechanical and rheological requirements while improving environmental and economic performance.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Concrete and Cement Materials Research · Recycled Aggregate Concrete Performance · Recycling and utilization of industrial and municipal waste in materials production

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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