Seismic performance-optimization of hybrid yielding dampers with shape-memory alloy

Mahsa Farazmand, Payam Asadi, Parinaz Izadiniya · Emerald · 2026

A new algorithm optimizes the design of hybrid dampers using shape-memory alloys to improve seismic performance in steel structures.

High AI ConfidenceStrong SourceConceptEarly Research

Plain English summary

This research introduces a low-cost algorithm aimed at optimizing the design of hybrid dampers that incorporate shape-memory alloys (SMAs) and steel plates. The method focuses on achieving uniform damage distribution in steel structures to enhance their performance during seismic events.

Why this matters

The ability to efficiently design hybrid dampers can lead to safer and more resilient buildings in earthquake-prone areas. By reducing the number of dampers needed while maintaining performance, this research can lower construction costs and improve structural integrity.

Key findings

  • The proposed method reduces the number of hybrid dampers needed by up to 12% for SMA and 18% for steel dampers.
  • The method maintains all design objectives while optimizing damper layout.
  • Uniform damage distribution (UDD) is applied for the first time in this context.
  • The algorithm is low-computational-cost, making it practical for real-world applications.
  • The study demonstrates efficiency in designing 4-, 8-, and 12-story steel frames.

What's new

The application of the UDD concept to optimize the design of hybrid dampers with SMAs is a new approach.

Limitations

The abstract does not provide details on the specific limitations of the proposed method or its applicability to other types of structures.

Commercial context

The research is in the conceptual stage and has not yet been demonstrated in practical applications.

Publication

Publisher
Emerald
Publication date
June 5, 2026
Research type
Paper

Tags

More on Shape-Memory Alloys

See all →
Shape-Memory Alloyspaper· Jul 15, 2026

Functional Shape Recovery Response of Heat-Treated NiTiCu SHAPE Memory Alloy Wire

Heat treatment of NiTiCu shape memory alloy wires enhances their shape recovery response, achieving a high recovery ratio.

Ümit Zeybek · Black Sea Journal of Engineering and ScienceLaboratory Research
Shape-Memory Alloyspaper· Jul 9, 2026

A Deformable Robot Based on Shape Memory Alloy and Phase Change Metal

This research presents a novel deformable robot that integrates Shape Memory Alloys and phase change metals for enhanced adaptability and locomotion.

Junwei Zhang, Bo Yuan +1 · WileyWorking Prototype
Shape-Memory Alloyspaper· Jul 8, 2026

Development of a Directional Vibrator Using Shape-Memory Alloy Wires

This paper proposes a compact directional vibrator using Shape-Memory Alloy wires for enhanced haptic feedback in virtual and augmented reality applications.

Yuto Kawahara, Renke Liu +1 · MDPI AGLaboratory Research
Shape-Memory Alloyspreprint· Jul 3, 2026

Martensitic Transformation in Crystal-Amorphous Superlattices of NiTi Shape Memory Alloy

Crystal-amorphous superlattices in NiTi are simulated to shift martensitic transformation behavior, boosting reversibility and stiffness while raising transformation critical stress.

Bhavna Singh, Shivam Tripathi · arXivSimulation
Shape-Memory Alloyspreprint· Jul 2, 2026

HVAF Spraying of NiTi Coatings: Microstructure, Phase Transformation and Shape Memory Behavior

HVAF spraying enables thick (100–300 µm) NiTi shape-memory alloy coatings on mild steel that can undergo martensitic transformation and exhibit thermal actuation and shape memory effects after annealing.

Sneha Samal, Shrikant Joshi +8 · arXivLaboratory Research
Shape-Memory Alloyspaper· Jun 30, 2026

Omnidirectional Shape Proprioception for Untethered Shape Memory Alloy‐Driven Soft Robotic Arms

This research presents a modular soft robotic arm using shape memory alloys for improved proprioception and adaptability in unstructured environments.

Yiming Ouyang, Zelin Chen +6 · WileyLaboratory Research
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