Blockchain-Assisted Gene Expression Programming for Transparent Optimization and Strength Prediction in Fly Ash-Based Geopolymer Concrete

Zilefac Ebenezer Nwetlawung and Yi-Hsin Lin ()
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Zilefac Ebenezer Nwetlawung: School of Civil Engineering, Southeast University, Nanjing 210042, China
Yi-Hsin Lin: School of Civil Engineering, Southeast University, Nanjing 210042, China

Sustainability, 2025, vol. 17, issue 18, 1-19

Abstract: The global construction industry faces growing pressure to minimize environmental impact while maintaining durable, high-performance building materials. Fly ash-based geopolymer concrete (GPC) provides a sustainable, low-carbon, durable, and high-performance alternative to ordinary Portland cement (OPC). However, challenges remain in accurately predicting its structural behavior, particularly flexural strength, under varying compositional and curing conditions. This study integrates a Blockchain-assisted Gene Expression Programming Framework (B-GEPF) to enhance reliability and traceability in durability assessments of fly ash-based GPC. Focusing on the silica modulus of alkaline activators, the framework aims to improve predictive accuracy for flexural strength and optimize durability performance. Flexural strength was evaluated under controlled alkaline activator conditions (8M sodium hydroxide with sodium silicate) and varying fine aggregate ratios (1:1.5, 1:2, 1:3). The predictive model captures complex nonlinear relationships among silica modulus, fly ash content, and flexural behavior. Results indicate that higher activator concentrations increase flexural strength, while fly ash improves workability, reduces heat of hydration, and sustains long-term strength through secondary reactions. The B-GEPF framework demonstrates potential to accelerate GPC formulation optimization, ensuring reproducibility, reliability, and industrial scalability. By combining AI-driven predictions with blockchain-based validation, this approach supports sustainable construction, quality assurance, regulatory compliance, and transparent stakeholder collaboration. The study highlights dual benefits of environmental sustainability and digital trust, positioning fly ash-based GPC as a durable, low-carbon, and verifiable solution for resilient infrastructure. This convergence of AI predictive modeling and blockchain-secured data governance offers a robust, scalable tool for designing, validating, and deploying eco-friendly construction materials.

Keywords: blockchain; gene expression programming; geopolymer concrete (GPC); fly ash; optimization; durability (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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