Optimization of Concrete Strength Incorporating Limestone Powder and Carbon Nanotubes for Enhancing Structural Performance

• Vikrant S Vairagade

  Department of Civil Engineering, Priyadarshini College of Engineering, Nagpur, Maharashtra, 440019, India

• Alaka Das

  Department of Civil Engineering, Priyadarshini College of Engineering, Nagpur, Maharashtra, 440019, India

• Pranita S Bhandari

  Department of Civil Engineering, Priyadarshini College of Engineering, Nagpur, Maharashtra, 440019, India

The incorporation of nanomodifiers and other additives into structural concrete has been demonstrated to enhance mechanical performance, economic efficiency, and environmental sustainability. This study aims to optimize concrete mixtures with limestone powder (LP) and carbon nanotubes (CNTs) to address the nonlinear relationship between mixture elements and mechanical properties. Gaussian Process Regression (GPR) with Bayesian Optimization is a method that predicts compressive, flexural, and chloride permeability while assessing model uncertainty. XGBoost prioritizes features, while a MOEA discovers Pareto-optimal solutions that balance performance, cost, and carbon footprint. The experimental program was conducted at a water-cement ratio ranging from 0.38 to 0.42, with LP replacement levels varying from 0% to 10% and CNT additions ranging from 0% to 0.5% by cement weight. Performance parameters are measured 28 days after casting and curing combinations under standard conditions. Predictive modeling generates combinations, feature interpretation isolates influential qualities, and evolutionary optimization finds optimal trade-offs. The most significant enhancements were observed in compressive strength, durability, and flexural behavior, with 7% LP and 0.2% CNT. This blend demonstrates compressive strengths exceeding 65 megapascals (MPa), flexural strengths approaching 10 MPa, and chloride permeability reductions greater than 15% compared to reference concrete. The integrated modeling framework demonstrates the efficacy of machine learning in efficiently exploring vast design domains, thereby reducing laboratory expenditures and testing times. CNTs have been shown to enhance fracture resistance, while LP has been demonstrated to promote compressive strength enhancement, as indicated by interpretability metrics. Comparisons have revealed that previous approaches were less accurate and sustainable. The findings support data-driven rational concrete mixture design in high-performance structural applications.

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The data that support the findings of this study are available from the corresponding author upon reasonable request.

• Building and Construction
• Structural Engineering

Copyright (c) 2025 Vikrant S Vairagade, Alaka Das, Pranita S Bhandari

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