Open Access

ARTICLE

Accurate Compressive Strength Prediction of Fly Ash Geopolymers Using Advanced Ensemble Models and Morris Analysis

Arslan Qayyum Khan¹, Muhammad Dawood Rasheed², Muhammad Huzaifa Naveed², Amorn Pimanmas^3,*

1 Department of Civil and Environmental Engineering, Florida International University, Miami, FL, USA
2 Department of Civil Engineering, The University of Lahore, Lahore, Pakistan
3 Department of Civil Engineering, Kasetsart University, Bangkok, Thailand

* Corresponding Author: Amorn Pimanmas. Email:

(This article belongs to the Special Issue: Machine Learning for Reinforced Concrete Structures: Modeling, Optimization, and Monitoring)

Computer Modeling in Engineering & Sciences 2026, 147(2), 21 https://doi.org/10.32604/cmes.2026.083654

Received 08 April 2026; Accepted 06 May 2026; Issue published 27 May 2026

Abstract

The construction industry’s substantial carbon footprint, primarily attributed to the production of Ordinary Portland Cement, necessitates a transition toward more sustainable alternatives. Geopolymer concrete (GPC), an innovative binder synthesized from industrial by-products like fly ash (FA), offers a promising low-carbon solution but is hindered by performance variability and a lack of standardized design protocols. This research addresses this critical barrier by developing robust predictive models for the compressive strength of FA-based GPC. Six machine learning algorithms, including Bagging, Categorical Boosting (CatBoost), K-Nearest Neighbors (KNN), LightGBM, Random Forest Regressor (RFR), and eXtreme Gradient Boosting (XGBoost), were developed and evaluated. The results demonstrate that the XGBoost model achieved superior predictive accuracy, with the lowest average errors (Mean squared error of 4.66, Mean absolute error of 1.42) and the highest average coefficient of determination (0.962). To enhance model interpretability, a Morris sensitivity analysis was conducted. The analysis quantitatively identified the coarse aggregate as the most influential parameter governing compressive strength, followed by key chemical precursors such as silicon dioxide (SiO₂) and aluminum oxide (Al₂O₃). These findings not only align with established material science principles but also validate the physical realism of the machine learning model. This study provides a reliable computational framework for predicting the performance of FA-based GPC, facilitating mix design optimization and accelerating the adoption of this sustainable material in modern construction.

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