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<p>This study addresses the urgent need for sustainable alternatives in the construction industry, which significantly contributes to global carbon emissions, particularly from cement production. The research focuses on assessing the impact of crumb rubber (CR) on the compressive strength (CS) of self-compacting concrete when combined with eco-friendly materials like fly ash and ground granulated blast furnace slag. Utilizing a dataset of 439 data points, the study employs four predictive models—Nonlinear Regression, Interaction, Pure Quadratic, and Artificial Neural Networks (ANN)—to evaluate CS under various mix ratios and curing periods. The findings highlight the ANN model as the most accurate for predicting CS, with CR content being a critical factor. Additionally, the study develops a power function model for predicting splitting-tensile strength, offering a valuable tool for future research and industry applications.</p>

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Compressive strength modeling of the eco-friendly self-compacting rubberized concrete with fly ash and ground granulated blast furnace slag additives

  • 期刊论文

  • 发表日期：

    2025年1月24日

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  • Aryan Far H. Sherwani,
  • Payam Ismael Abdulrahman,
  • Khaleel H. Younis,
  • Ahmed Salih Mohammed,
  • Yarivan J. Zrar

摘要

This study addresses the urgent need for sustainable alternatives in the construction industry, which significantly contributes to global carbon emissions, particularly from cement production. The research focuses on assessing the impact of crumb rubber (CR) on the compressive strength (CS) of self-compacting concrete when combined with eco-friendly materials like fly ash and ground granulated blast furnace slag. Utilizing a dataset of 439 data points, the study employs four predictive models—Nonlinear Regression, Interaction, Pure Quadratic, and Artificial Neural Networks (ANN)—to evaluate CS under various mix ratios and curing periods. The findings highlight the ANN model as the most accurate for predicting CS, with CR content being a critical factor. Additionally, the study develops a power function model for predicting splitting-tensile strength, offering a valuable tool for future research and industry applications.