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<p>Achieving fracture toughness and sustainability in concrete remains a major challenge in modern construction. This study explores the synergistic effects of graphene nano-platelets (GNPs) and date palm fiber (DPF) on the mechanical and eco-efficiency performance of concrete. Eleven mix proportions with varying GNP (0–0.2%) and DPF (0–2.5%) contents were tested. Response surface methodology was employed to model five key properties—compressive strength, flexural strength, modulus of elasticity, split tensile strength, and ultrasonic pulse velocity. Pearson and canonical correlation analyses were used to examine property interrelations. Multi-objective optimization indicated that incorporating 0.199% GNPs and 0.99% DPF achieved optimal performance, enhancing compressive strength by 44%. The developed models exhibited high accuracy (R² &gt; 0.96, desirability = 0.971). While GNPs increased embodied carbon, the overall approach balances performance, modeling precision, and sustainability, offering valuable insights for next-generation green infrastructure materials.</p>

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Response surface modeling and correlation analyses of mechanical and non-destructive properties in graphene–date palm fiber reinforced concrete

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月17日

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  • Ahmed A. Abdou Elabbasy,
  • Abdulrazak H. Almaliki,
  • Muhammad Basit Khan,
  • Paul O. Awoyera,
  • Olaolu George Fadugba

摘要

Achieving fracture toughness and sustainability in concrete remains a major challenge in modern construction. This study explores the synergistic effects of graphene nano-platelets (GNPs) and date palm fiber (DPF) on the mechanical and eco-efficiency performance of concrete. Eleven mix proportions with varying GNP (0–0.2%) and DPF (0–2.5%) contents were tested. Response surface methodology was employed to model five key properties—compressive strength, flexural strength, modulus of elasticity, split tensile strength, and ultrasonic pulse velocity. Pearson and canonical correlation analyses were used to examine property interrelations. Multi-objective optimization indicated that incorporating 0.199% GNPs and 0.99% DPF achieved optimal performance, enhancing compressive strength by 44%. The developed models exhibited high accuracy (R² > 0.96, desirability = 0.971). While GNPs increased embodied carbon, the overall approach balances performance, modeling precision, and sustainability, offering valuable insights for next-generation green infrastructure materials.