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Babatunde Abiodun Salami1, Jamilu Usman2, Afeez Gbadamosi3

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This study predicts concrete compressive strength using blended binders like ground granulated blast furnace slag (GGBFS) and fly ash (FA). Support vector regression (SVR) with specific input variables accurately estimated concrete performance, offering a sustainable alternative to traditional cement.

Keywords:
Blended concreteGround granulated blast furnace slagMachine learningRobust linear regressionSupport vector regression

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Area of Science:

  • Materials Science and Engineering
  • Sustainable Construction Materials
  • Computational Materials Science

Background:

  • Growing demand for reduced embodied carbon in cement and sustainable construction practices necessitates exploring alternative binders.
  • Ground granulated blast furnace slag (GGBFS) and fly ash (FA) are promising supplementary cementitious materials (SCMs) that can enhance concrete properties.
  • Accurate prediction of concrete compressive strength (CS) is vital for structural integrity and material optimization.

Purpose of the Study:

  • To estimate the compressive strength of concrete utilizing a blend of GGBFS, FA, and ordinary Portland cement.
  • To evaluate the efficacy of kernel regression techniques, specifically Support Vector Regression (SVR), Robust Linear Regression (RLR), and Multi-Linear Regression (MLR), for predicting concrete compressive strength.
  • To identify the optimal combination of input variables for accurate compressive strength prediction using machine learning models.

Main Methods:

  • Utilized a dataset of 3323 concrete mix samples with eight input variables: cement, FA, GGBFS, water, superplasticizer (SP), coarse aggregate (CA), fine aggregate (Fag), and age.
  • Employed linear correlation analysis to assess the relative importance of input features.
  • Trained and evaluated three kernel-based models (SVR, RLR, MLR) using three distinct input variable combinations to predict compressive strength (CS).

Main Results:

  • Combination III, incorporating cement, water, FA, SP, CA, GGBFS, and Fag, yielded the best predictive performance across all tested regression models.
  • Support Vector Regression (SVR) demonstrated superior accuracy, achieving an R2 value of 0.984 and a Mean Squared Error (MSE) of 0.0019 with Combination III.
  • The developed prediction models showed high accuracy within the range of input variables used in the study.

Conclusions:

  • Support Vector Regression (SVR) combined with a comprehensive set of input variables (Combination III) is highly effective and efficient for predicting the compressive strength of blended concrete.
  • The findings support the use of SVR models for optimizing concrete mix designs incorporating SCMs like GGBFS and FA.
  • The study highlights the potential of data-driven approaches in developing sustainable construction materials and reducing reliance on traditional Portland cement.