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<p>In recent years, nanomaterial have garnered significant interest for enhancing engineered cementitious composites (ECC). This study employs response surface methodology (RSM) to conduct multi-objective optimization of the mechanical properties of nanomaterial-reinforced ECC (NR-ECC), aiming to determine the optimal dosages of silica nanoparticles (NS) and carbon nanotubes (CNTs). A central composite design (CCD) was utilized to formulate 13 mixtures with varying NS (1–3%) and CNTs (0.1–0.2%) contents. Three quadratic response surface models were developed and validated to predict uniaxial compressive strength, uniaxial tensile strength, and peak tensile strain, demonstrating high accuracy (<i>R</i><sup>2</sup> = 0.94–0.98) and statistical significance (<i>p</i> &lt; 0.05). Multi-objective optimization identified the optimal contents as 1.698% NS and 0.155% CNTs, which were experimentally validated with errors below 5%. The results indicate that NS enhances matrix density and interfacial properties, while CNTs facilitate multi-scale crack bridging. The optimal mixture improved compressive strength, tensile strength, and tensile strain by 9.89%, 27.75%, and 32.45%, respectively, compared to the baseline. This study provides a reliable modeling and optimization framework that supports the efficient design of high-performance NR-ECC for practical engineering applications.</p>

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Multi-objective optimization on mechanical properties of nanomaterial-reinforced cementitious composites using response surface methodology (RSM)

  • 期刊论文

  • 发表日期：

    2025年10月8日

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  • Deyi Liu,
  • Xutao Zhang,
  • Xikuan Lyu

摘要

In recent years, nanomaterial have garnered significant interest for enhancing engineered cementitious composites (ECC). This study employs response surface methodology (RSM) to conduct multi-objective optimization of the mechanical properties of nanomaterial-reinforced ECC (NR-ECC), aiming to determine the optimal dosages of silica nanoparticles (NS) and carbon nanotubes (CNTs). A central composite design (CCD) was utilized to formulate 13 mixtures with varying NS (1–3%) and CNTs (0.1–0.2%) contents. Three quadratic response surface models were developed and validated to predict uniaxial compressive strength, uniaxial tensile strength, and peak tensile strain, demonstrating high accuracy (R2 = 0.94–0.98) and statistical significance (p < 0.05). Multi-objective optimization identified the optimal contents as 1.698% NS and 0.155% CNTs, which were experimentally validated with errors below 5%. The results indicate that NS enhances matrix density and interfacial properties, while CNTs facilitate multi-scale crack bridging. The optimal mixture improved compressive strength, tensile strength, and tensile strain by 9.89%, 27.75%, and 32.45%, respectively, compared to the baseline. This study provides a reliable modeling and optimization framework that supports the efficient design of high-performance NR-ECC for practical engineering applications.