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<p>Typical optimization approaches fail to respond to changing load conditions and structural response nonlinearity limit tensile truss design. Adaptive tensile learning and swarm-driven material redistribution improve structural adaption and efficiency in the bioinspired metaheuristic framework. The model increases convergence, minimizes material, and maintains equilibrium fidelity across load circumstances. Comparisons reveal 35% less material and 30% faster convergence than benchmark metaheuristics. These findings enable intelligent, self-regulating tensegrity systems for deployable architecture, aeronautical constructions, and adaptive robotics. The integrated framework saves about 35% of materials, improves accommodation by around 40%, and shortens convergence time by nearly 28% when compared with traditional meta-heuristic methods. It also allows quick adaptations in geometry, self-healing from damage, and dynamic balance of this stress in tension or compression. This paper opens a novel path for the next generation of intelligent bioinspired tensegrity systems and deep structural awareness for applications in deployable architecture, aerospace structures, and soft robotics.</p>

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Hybrid machine learning modeling and bioinspired metaheuristic optimization of tensegrity trusses for structural design

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月19日

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  • Sangita Meshram,
  • Pallavi S.Chakole,
  • Snehal K. Kamble,
  • Vaishali Mendhe,
  • Sham H. Mankar,
  • Lowlesh N. Yadav,
  • Tejas R. Patil,
  • Nischal Puri,
  • Rohit Pawar,
  • Manda Ukey

摘要

Typical optimization approaches fail to respond to changing load conditions and structural response nonlinearity limit tensile truss design. Adaptive tensile learning and swarm-driven material redistribution improve structural adaption and efficiency in the bioinspired metaheuristic framework. The model increases convergence, minimizes material, and maintains equilibrium fidelity across load circumstances. Comparisons reveal 35% less material and 30% faster convergence than benchmark metaheuristics. These findings enable intelligent, self-regulating tensegrity systems for deployable architecture, aeronautical constructions, and adaptive robotics. The integrated framework saves about 35% of materials, improves accommodation by around 40%, and shortens convergence time by nearly 28% when compared with traditional meta-heuristic methods. It also allows quick adaptations in geometry, self-healing from damage, and dynamic balance of this stress in tension or compression. This paper opens a novel path for the next generation of intelligent bioinspired tensegrity systems and deep structural awareness for applications in deployable architecture, aerospace structures, and soft robotics.