AI-Driven Optimization of Fly Ash-Based Geopolymer Concrete for Sustainable High Strength and CO2 Reduction: An Application of Hybrid Taguchi–Grey–ANN Approach

The construction industry urgently requires sustainable alternatives to conventional concrete to reduce its environmental impact. This study addresses this challenge by developing machine learning-optimized geopolymer concrete (GPC) using industrial waste fly ash as cement replacement. An integrated Taguchi–Grey relational analysis (GRA) and artificial neural network (ANN) approach was developed to simultaneously optimize mechanical properties and environmental performance. The methodology analyzes over 1000 data points from 83 studies to identify key mix parameters including fly ash content, NaOH/Na2SiO3 ratio, and curing conditions. Results indicate that the optimized FA-GPC formulation achieves a 78% reduction in CO2 emissions, decreasing from 252.09 kg/m3 (GRC rank 1) to 55.0 kg/m3, while maintaining a compressive strength of 90.9 MPa. The ANN model demonstrates strong predictive capability, with R2 > 0.95 for strength and environmental impact. Life cycle assessment reveals potential savings of 3941 tons of CO2 over 20 years for projects using 1000 m3 annually. This research provides a data-driven framework for sustainable concrete design, offering practical mix design guidelines and demonstrating the viability of fly ash-based GPC as high-performance, low-carbon construction material.