Predictive Regression Models for the Compressive Strength of Fly Ash-based Alkali-Activated Cementitious Materials via Machine Learning

Authors

  • H. A. Maddah Department of Chemical Engineering, King Abdulaziz University, Saudi Arabia
  • M. Kheimi Department of Civil Engineering, King Abdulaziz University, Saudi Arabia
  • M. Abo El-Wafa Department of Civil Engineering, King Abdulaziz University, Saudi Arabia

Abstract

Fly ash powders produced from pulverized carbon are a promising renewable and sustainable replacement for Ordinary Portland Cement (OPC) in concrete. However, quantifying the desired compressive strength threshold requires defining the ratio of Fly Ash (FA) to fine aggregates (S). This study presents two novel machine learning models to predict the mechanical properties of FA-based Alkali-Activated Cementitious Materials (AACMs) using supervised regressors. The two models, SLR and MGSVM, showed high prediction accuracy (~95%) based on raw compressive strength training datasets from AACMs with mixed proportions of FA/S (0, 5, 10, 15, 20, 25, and 30%) for 28 days of curing. Maximum compressive strength of ~67.5MP was observed at approximately 20% FA/S (spline interpolation), suggesting the attainment of high mechanical stability. Having more than 30% FA/S indicates a high probability of recovering the original strength of 61MPa for pristine AACMs. The non-linear stress or strain patterns against FA/S confirmed the applicability of stress-strain relationships and elasticity laws. The pozzolanic properties of FA facilitate interaction with Ca(OH)2 for aggregation linked to the non-linear behavior. This study provides generalized design models for correlating the mix proportions in OPC-substituted AACMs to the optimum compressive strength.

Keywords:

regression, machine learning, compressive strength, fly ash, mechanical stability, cementitious materials

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H. A. Maddah, M. Kheimi, and M. Abo El-Wafa, “Predictive Regression Models for the Compressive Strength of Fly Ash-based Alkali-Activated Cementitious Materials via Machine Learning ”, Eng. Technol. Appl. Sci. Res., vol. 12, no. 2, pp. 8241–8247, Apr. 2022.

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