article · Scientific Reports
The construction industry relies heavily on cement, which generates substantial carbon emissions and consumes high levels of embodied energy. Traditional concrete design often depends on trial and error, failing to balance structural durability with environmental impact. To solve this, a data-driven framework was created to assess and optimise sustainable concrete formulations across thirteen material variables and thirteen performance indicators. The approach integrates a Genetic Algorithm with the TOPSIS multi-criteria decision-making method to evaluate supplementary cementitious materials. The optimisation identified a specific formulation, designated MIX_009, incorporating fly ash, silica fume, metakaolin, rice husk ash, and alccofine. This optimal blend reduces embodied carbon dioxide by approximately 257.59 kilograms per cubic metre and embodied energy by around 1,545 megajoules per cubic metre while achieving the highest overall performance ranking.
Traditional concrete production is a major driver of global carbon emissions and energy consumption. By replacing trial-and-error design methods with algorithmic optimisation, the construction sector can reliably incorporate industrial by-products and alternative binders. This lowers the ecological footprint and embodied energy of building materials while maintaining necessary structural reliability.
This tool can assist concrete manufacturers, civil engineering contractors, and green building developers in formulating low-carbon mixes. The framework represents applied and tested computational research with a validated optimal recipe. Real-world commercial use would require standard compliance testing, practical batch validation under field conditions, and integration into existing commercial concrete design software.
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One of the biggest consumers of cement is the construction sector, which contributes significantly to overall carbon emissions and embodied energy around the globe. Conventional selection of concrete materials is typically based on trial and error, which does not adequately address the conflicting objectives of mechanical performance, durability, and environmental sustainability. This led to an urgent demand for systematic, data-driven approaches that ensure both structural reliability and a reduced ecological footprint in green building construction. This study addresses this challenge by developing a tool for sustainable material assessment and optimization of the material selection in this aspect, combining 13 variables of material composition and 13 indicators of concrete performance. This study achieves this by integrating high material evaluation, algorithm-based optimization, and multi-criteria decision making to create a replicable and scalable model for designing sustainable concrete mixes. Using data analytics and nature-inspired algorithm, such as the Genetic Algorithm (GA), this study optimizes concrete performance by adding of sustainable materials or supplementary cementitious materials (SCMs). The GA algorithm effectively identifies MIX_009 containing 38 kg/m 3 , 8 kg/ m 3 , 15 kg/ m 3 , 10 kg/ m 3 , 5 kg/ m 3 of fly ash, silica fume, metakaolin, rice husk ash and alccofine as sustainable materials can achieve a remarkable saving in embodied CO 2 (approximately 257.59 kg/m 3 ) and embodied energy (~ 1545 MJ/m 3 ) as optimal mix with best fitness values of − 0.3605. In addition, the TOPSIS also confirmed the strength of the GA output, ranking MIX_009 as the best with a closeness coefficient of 0.810, affirming it as the most balanced option in terms of performance and sustainability. The findings guarantee that green building materials can be developed with both scientific rigour and practical applicability, addressing urgent sustainability challenges in the construction sector.
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DOI: 10.1038/s41598-026-70594-3
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