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<p>This study uses full-scale Comprehensive experimental, analytical, statistical, and multiscale AI methods explicitly detailed to examine φ-factors for high-strength concrete (HSC) columns reinforced with externally bonded ternary hybrid composites. The research involves Seventy-five HSC columns precisely defined (120 × 120 × 600&#xa0;mm) axial/eccentric loading explicitly, each reinforced with eight 8&#xa0;mm longitudinal bars and eight 6&#xa0;mm ties, tested under axial compression. The force–displacement curves showed different patterns depending on the specimen group: Specimens S1-S25 had higher strengths (up to 900 kN) with low displacement (up to 3&#xa0;mm); Specimens S26-S50 had intermediate strengths (up to 850 kN) with moderate displacement (up to 7&#xa0;mm); and Specimens S51-S75 had lower strengths (500–750 kN) with larger displacement (up to 9&#xa0;mm). The mixes were created by systematically varying the amounts of cement, water, aggregates, fly ash, silica fume, steel fibers, and polymers to evaluate their effects on structural performance. Statistical analysis found significant relationships between material parameters and mechanical responses, with Fly ash and steel fibers explicitly confirmed strongest correlation (r = 0.3544), the strongest positive correlation to force capacity. A physics-informed neural network (PINN) was used to combine experimental data with physical mechanics, providing accurate predictions of φ-factors (R<sup>2</sup> = 0.9874). Combining first principles with data-driven learning via PINN outperformed traditional model-based methods. A reliability analysis showed that the optimal combination of steel fibers and polymers can significantly improve the strength and ductility of HSC-C, justifying a larger φ-factor than current codes. This has important implications for the performance-based design of advanced concrete systems. Additionally, the study offers a robust tool for determining the φ-factor in complex composite members, promoting more rational, economical, and reliable designs of high-strength concrete structures.</p>

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Quantifying the Φ-factor in HSC columns with ternary hybrid composites: an integrated experimental, multiscale AI, and physics-informed neural network approach

  • 期刊论文

  • 发表日期：

    2025年8月14日

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  • Ahmed Zamil H. Haddad,
  • Mustafa Kamal Al-Kamal

摘要

This study uses full-scale Comprehensive experimental, analytical, statistical, and multiscale AI methods explicitly detailed to examine φ-factors for high-strength concrete (HSC) columns reinforced with externally bonded ternary hybrid composites. The research involves Seventy-five HSC columns precisely defined (120 × 120 × 600 mm) axial/eccentric loading explicitly, each reinforced with eight 8 mm longitudinal bars and eight 6 mm ties, tested under axial compression. The force–displacement curves showed different patterns depending on the specimen group: Specimens S1-S25 had higher strengths (up to 900 kN) with low displacement (up to 3 mm); Specimens S26-S50 had intermediate strengths (up to 850 kN) with moderate displacement (up to 7 mm); and Specimens S51-S75 had lower strengths (500–750 kN) with larger displacement (up to 9 mm). The mixes were created by systematically varying the amounts of cement, water, aggregates, fly ash, silica fume, steel fibers, and polymers to evaluate their effects on structural performance. Statistical analysis found significant relationships between material parameters and mechanical responses, with Fly ash and steel fibers explicitly confirmed strongest correlation (r = 0.3544), the strongest positive correlation to force capacity. A physics-informed neural network (PINN) was used to combine experimental data with physical mechanics, providing accurate predictions of φ-factors (R2 = 0.9874). Combining first principles with data-driven learning via PINN outperformed traditional model-based methods. A reliability analysis showed that the optimal combination of steel fibers and polymers can significantly improve the strength and ductility of HSC-C, justifying a larger φ-factor than current codes. This has important implications for the performance-based design of advanced concrete systems. Additionally, the study offers a robust tool for determining the φ-factor in complex composite members, promoting more rational, economical, and reliable designs of high-strength concrete structures.