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A three-stage damage detection method for large-scale space structures using forward substructuring approach and enhanced bat optimization algorithm

机译:基于前向子结构和改进的蝙蝠优化算法的大型空间结构三阶段损伤检测方法

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摘要

In this study, an efficient three-stage method is proposed for damage detection of large-scale space structures by employing forward substructuring approach, modal strain energy and enhanced bat algorithm (EBA) optimization. EBA is a modified version of BA that is proposed in this paper and used a passive congregation operator to improve the performance of standard BA. In the first stage, the global structure is divided into manageable substructures. The stiffness matrices of independent substructures are obtained based on Kron's substructuring method. Then a modal strain energy-based index is employed to precisely locate the eventual damages of the structure. In the third stage, damage severities are estimated via EBA using the second-stage results. To demonstrate the ability of the proposed method for detection of multiple structural damages, large-scale space structures with different types of damage scenarios are considered. The results show that the proposed method can detect the exact locations and severity of damages highly accurate in space structures.
机译:在这项研究中,提出了一种有效的三阶段方法,该方法采用前向子结构化方法,模态应变能和增强蝙蝠算法(EBA)优化来检测大型空间结构的损伤。 EBA是本文提出的BA的修改版本,它使用被动会聚运算符来提高标准BA的性能。在第一阶段,全局结构分为可管理的子结构。基于Kron的子结构化方法,获得了独立子结构的刚度矩阵。然后,采用基于模态应变能的指标来精确定位结构的最终损伤。在第三阶段,使用第二阶段结果通过EBA估算损坏严重程度。为了证明所提出的方法检测多种结构损伤的能力,考虑了具有不同类型损伤场景的大型空间结构。结果表明,所提出的方法可以准确地检测出空间结构中损伤的确切位置和严重程度。

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