ANN Approach to Forecasting the Strength of Nano Silica Incorporated Geopolymer Composite

Year : 2025 | Volume : 13 | Special Issue 03 | Page : 267 278
    By

    Sagar Paruthi,

  • Ibadur Rahman,

  • Asif Husain,

  1. Assistant Professor, School of Architecture & Design, K. R. Mangalam University, Gurugram, Haryana, India
  2. Assistant Professor, Department of Civil Engineering, Jamia Millia Islamia, New Delhi, India
  3. Professor, Department of Civil Engineering, Jamia Millia Islamia, New Delhi, India

Abstract

Coal and steel industry by-products, such as fly ash (FA) and blast furnace slag (GGBS), have gained significant attention as precursors for geopolymer concrete (GPC) due to their high aluminosilicate content, offering a sustainable alternative to conventional cement. Nano silica (NS), recognized for its exceptional pozzolanic activity and ability to refine microstructure, has shown potential to enhance the mechanical and durability properties of GPC. This study investigates the influence of NS incorporation at varying percentages (1%, 1.5%, 2%) in GPC, cured at different temperatures (27 0C, 60 0C, 90 0C, 120 0C), on compressive strength (CS), split tensile strength (STS) and flexural strength (FS). Experimental results reveal that GPC cured at 90 0C for 28 days exhibits the highest strengths i.e. CS of 55.96 MPa, STS of 5.63 MPa, and FS pf 5.66 MPa, demonstrating the effectiveness of heat curing in optimizing mechanical properties. However, the experimental determination of GPC properties is resource intensive. This research introduces machine learning, especially artificial neural networks (ANN), as a predictive tool. The ANN models, trained on 20 GPC mix designs, accurately predict CS (R2 = 0.9797), STS (R2 = 0.9847), and FS (R2 = 0.9864), highlighting their reliability. Compared to traditional modeling approaches, ANN offers superior prediction accuracy, aiding in efficient material proportioning and optimization. The novelty of this research lies in combining advanced nanotechnology and machine learning to address the dual challenges of sustainability and performance in GPC, bridging experimental and computational domains for future infrastructure applications.

Keywords: ANN, nano silica, geopolymer concrete, heat cured, compressive strength.

[This article belongs to Special Issue under section in Journal of Polymer and Composites (jopc)]

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How to cite this article:
Sagar Paruthi, Ibadur Rahman, Asif Husain. ANN Approach to Forecasting the Strength of Nano Silica Incorporated Geopolymer Composite. Journal of Polymer and Composites. 2025; 13(03):267-278.
How to cite this URL:
Sagar Paruthi, Ibadur Rahman, Asif Husain. ANN Approach to Forecasting the Strength of Nano Silica Incorporated Geopolymer Composite. Journal of Polymer and Composites. 2025; 13(03):267-278. Available from: https://journals.stmjournals.com/jopc/article=2025/view=210231


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Special Issue Subscription Original Research
Volume 13
Special Issue 03
Received 12/11/2024
Accepted 04/02/2025
Published 25/04/2025
Publication Time 164 Days
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