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<p>This study explores the performance of ground granulated blast furnace slag (GGBS)-based geopolymer concrete (GPC), with particular attention to the effects of incorporating micro material i.e. volcanic pumice dust (VPD), cement kiln dust (CKD), and nanomaterial i.e. nano-silica (NS) as partial replacements for GGBS. In this study, GGBS was independently replaced with CKD and VPD at 10%, 20%, and 30%, and with NS at 1%, 2%, and 3% by weight. Mechanical properties including compressive, split tensile, and flexural strength were evaluated, revealing significant enhancements due to the synergistic effect of micro and nano materials. Durability assessment included chemical attack, rapid chloride penetrability, and bulk diffusion tests, confirming improved resistance to aggressive environments. Furthermore, artificial neural network (ANN) and adaptive neuro-fuzzy interference system (ANFIS) models were developed to predict the compressive strength using experimental data. Among these models, ANFIS demonstrated superior prediction, with a high coefficient of correlation (R) value of 0.9874, variance accounted for (VAF) 98.21, and Nash Sutcliffe efficiency (NSE) 0.981. This study underscores the potential of NS in improving the GGBS-based GPC properties.</p> Graphical abstract <p></p>

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Influence of cement kiln dust, volcanic pumice dust, and nano silica in heat-cured GGBS-based geopolymer concrete: experimental and predictive modeling

  • 期刊论文

  • 发表日期：

    2025年7月7日

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  • Afzal Husain Khan,
  • Sagar Paruthi,
  • Ali Almalki,
  • Hassan M. Magbool

摘要

This study explores the performance of ground granulated blast furnace slag (GGBS)-based geopolymer concrete (GPC), with particular attention to the effects of incorporating micro material i.e. volcanic pumice dust (VPD), cement kiln dust (CKD), and nanomaterial i.e. nano-silica (NS) as partial replacements for GGBS. In this study, GGBS was independently replaced with CKD and VPD at 10%, 20%, and 30%, and with NS at 1%, 2%, and 3% by weight. Mechanical properties including compressive, split tensile, and flexural strength were evaluated, revealing significant enhancements due to the synergistic effect of micro and nano materials. Durability assessment included chemical attack, rapid chloride penetrability, and bulk diffusion tests, confirming improved resistance to aggressive environments. Furthermore, artificial neural network (ANN) and adaptive neuro-fuzzy interference system (ANFIS) models were developed to predict the compressive strength using experimental data. Among these models, ANFIS demonstrated superior prediction, with a high coefficient of correlation (R) value of 0.9874, variance accounted for (VAF) 98.21, and Nash Sutcliffe efficiency (NSE) 0.981. This study underscores the potential of NS in improving the GGBS-based GPC properties.

Graphical abstract