Prestressing and Self-Healing of Fiber-Reinforced and Ultra-High-Performance Concrete Using Shape Memory Alloys

Alexander Chen, Bassem Andrawes · MDPI AG · 2026

This study explores the use of shape memory alloys for prestressing and self-healing in fiber-reinforced and ultra-high-performance concrete.

High AI ConfidenceStrong SourceLaboratory ResearchEarly Research

Plain English summary

This research focuses on enhancing the performance of fiber-reinforced concrete (FRC) and ultra-high-performance concrete (UHPC) by using shape memory alloy (SMA) bars. The study evaluates how SMA bars can prestress concrete to prevent cracking and heal existing cracks when activated. Various heating methods for the SMA bars are also tested to determine their effectiveness.

Why this matters

Improving the durability and longevity of concrete structures is crucial for reducing maintenance costs and enhancing safety. This research could lead to more resilient construction materials that can self-repair, potentially transforming how we approach concrete design and maintenance.

Key findings

  • SMA bars can effectively prestress FRC and UHPC, delaying crack formation.
  • SMA bars can heal cracks in concrete, reducing crack width significantly.
  • Reductions in crack width of up to 90% were observed in the tested specimens.
  • Different heating methods for SMA activation were compared for efficiency and safety.
  • The study fills a knowledge gap in the use of SMA in advanced concrete types.

What's new

This study is novel in its experimental evaluation of SMA bars specifically in fiber-reinforced and ultra-high-performance concrete, as previous research focused on conventional concrete.

Limitations

The abstract does not provide details on the scale of the experiments or long-term performance assessments of the SMA in concrete.

Commercial context

The research is still in the experimental phase and has not yet been commercialized.

Publication

Publisher
MDPI AG
Publication date
March 25, 2026
Research type
Paper
License
https://creativecommons.org/licenses/by/4.0/

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Method note: Summaries and ratings on this page are generated by AI from the abstract only. Read the original paper for full context. · Model: gpt-4o-mini-2024-07-18