ArticleScientific reports2026
Machine learning-driven evaluation of mechanical and microstructural properties of agro-waste-derived geopolymer concrete.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Corrections and comments
- Retracted
Authors and funding
6 authors.
Funding
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Abstract
The growing demand for sustainable construction materials has accelerated research into eco-friendly alternatives to traditional Portland cement. This research explores the potential of geopolymer concrete formulated from agricultural-waste ashes as a sustainable replacement for conventional Portland cement. Banana peel ash (BPA) and sugarcane bagasse ash (SCBA) were employed as aluminosilicate precursors, and their combined effects were systematically examined through controlled variations in blend proportion, alkaline activator molarity, sodium silicate-to-sodium hydroxide (SS/SH) ratio, and aggregate-to-binder ratio. The influence of these parameters on fresh and hardened properties-including workability, compressive strength, and flexural strength-was rigorously evaluated. Within the defined experimental domain, an optimal formulation comprising 52.5% SCBA and 47.5% BPA activated with 10 M NaOH achieved compressive and flexural strengths of 33.17 MPa and 9.95 MPa, respectively, demonstrating structural-grade performance suitable for practical applications. Detailed microstructural investigations employing SEM-EDS, XRD, FTIR and TGA techniques confirmed that both ashes exhibit high silica content, significant pozzolanic behaviour, and that increased activator concentration enhanced the dissolution of aluminosilicate phases leading to a denser geopolymeric matrix with improved durability. To further strengthen the analytical framework and enable predictive mix optimization, artificial intelligence-based models-Gene Expression Programming (GEP) and Artificial Neural Networks (ANN)-were developed. Both models achieved excellent predictive performance (R
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