Journal of Applied Science and Engineering

Published by Tamkang University Press

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Machine Learning-Assisted Strength Estimation of Silica Rich Geopolymer Composites: A Green Material Perspective

 2026

 Volume 35

Weiying Wu

Xinyang Vocational and Technical College, Xinyang, Henan, 464000, China

Received: January 28, 2026
Accepted: June 19, 2026
Publication Date: September 25, 2026

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The impact of each attribute on the target using SHAP analysis

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This study investigates the capability of machine learning (ML) techniques to predict the unconfined compressive strength (UCS) of sandy soil composite grains stabilized with natural pozzolanic geopolymer. Geopolymer stabilization is employed to boost the performance of soils with fundamentally feeble mechanical qualities for engineering applications. An Extremely Randomized Trees (ERT) model is developed using a comprehensive dataset compiled from existing literature. To improve model performance, key hyperparameters are optimized using metaheuristic algorithms, namely Artificial Protozoa (AP) and Coati (Co) optimization techniques. The models are trained and evaluated based on multiple input variables influencing UCS. The results indicate that both optimized ERT models provide reliable and accurate predictions, with the AP-based approach demonstrating comparatively superior performance. Overall, the findings highlight the effectiveness of integrating ML models with metaheuristic optimization for predicting the strength characteristics of geopolymer-stabilized soils.

Keywords: Composite materials; Pozzolanic geopolymer; Unconfined compressive strength; ML; ERT

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