What question did this study set out to answer?

The study aims to optimize sustainable high-performance concrete through experimentation and data-driven methods using innovative materials.

April 1, 2026Open Access

Data-driven optimisation of sustainable high-performance concrete incorporating SCMs, biomass ash, and graphene nanoplatelets

Key Points

The study aims to optimize sustainable high-performance concrete through experimentation and data-driven methods using innovative materials.
Used a hybrid binder featuring fly ash, GGBS, biomass ash, and graphene nanoplatelets.
Prepared M40 grade concrete mixtures with varied proportions of supplementary materials and additives.
Evaluated mechanical properties, rapid chloride permeability, water uptake, and thermal stability after heat exposure.
Applied machine learning models like Random Forest, XGBoost, and CNN LSTM for predictive analysis.
Employed multi-objective optimization to balance strength, durability, and cost.
Achieved compressive strength of 55 MPa, 23% higher than control mix.
Demonstrated 42% lower chloride permeability and 40% lower water absorption in optimized mix.
Maintained above 80% strength after exposure to 300 °C, indicating improved thermal stability.
XGBoost showed best predictive accuracy (R2 > 0.95) for forecasting strength.
Identified biomass ash content and SCM balance as crucial factors for performance.

Abstract

The current study outlines an integrated experimental and data-driven methodology of the development of sustainable high-performance concrete, using a hybrid low-carbon binder, which includes fly ash (FA), ground granulated blast-furnace slag (GGBS), thermally treated coir biomass (TTCB) and graphene nanoplatelets (GNPs). M40 grade concrete mixtures were prepared by systematic variation of supplementary cementitious materials (SCMs, 30% of the total binder), TTCB (5% to 10%) and GNPs dosage (0.08% to 0.12%). Mechanical properties were evaluated at 7 and 28 days, rapid chloride permeability (RCPT), water uptake and residual strength after exposure to 300 °C. The optimised mix delivered a compressive strength of 55 MPa at 28 days, which is approximately 23% greater than the control mix (44–45 MPa at 28 days), and chloride permeability 505 C (42% lower) and water absorption 2.8% (40% lower), respectively. Retention of strength following exposure to 300 °C was above 80%, which means that the thermal stability was improved. Microstructural examinations confirmed refined pore structure, lower content of portlandite and enhanced interfacial bonding. The 60 experimental observations based on replicated specimens in 10 mix designs were trained on Random Forest, XGBoost and CNN LSTM models. XGBoost had the best predictive accuracy (R2 > 0.95 to predict strength), and the permutation-importance analysis revealed TTCB content and SCMs balance to be the most important predictors. Multi-objective optimisation (NSGA-II and MOEA/D) was used to produce trade-offs between strength, durability, embodied CO2 and cost within the constrained experimental space. The suggested surrogate-assisted optimisation model offers a repeatable approach to eco-efficient concrete mix design at limited laboratory conditions.

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