Work

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

Year: 2026

Type: article

Abstract: 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.

Source: Journal of Engineering and Applied Science

DOI: 10.1186/s44147-026-00873-3