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首页> 外文期刊>Mechanical systems and signal processing >An hybrid real genetic algorithm to detect structural damage using modal properties
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An hybrid real genetic algorithm to detect structural damage using modal properties

机译：一种使用模态特性检测结构损伤的混合实遗传算法

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摘要

An hybrid real-coded Genetic Algorithm with damage penalization is implemented to locate and quantify structural damage. Cenetic Algorithms provide a powerful tool to solved optimization problems. With an appropriate selection of their operators and parameters they can potentially explore the entire solution space and reach the global optimum. Here, the set-up of the Cenetic Algorithm operators and parameters is addressed, providing guidelines to their selection in similar damage detection problems. The performance of five fundamental functions based on modal data is studied. In addition, this paper proposes the use of a damage penalization that satisfactorily avoids false damage detection due to experimental noise or numerical errors. A tridimensional space frame structure with single and multiple damages scenarios provides an experimental framework which verifies the approach. The method is tested with different levels of incompleteness in the measured degrees of freedom. The results show that this approach reaches a much more precise solution than conventional optimization methods. A scenario of three simultaneous damage locations was correctly located and quantified by measuring only a 6.3% of the total degrees of freedom.
机译：实施了带有损伤惩罚的混合实码遗传算法，以定位和量化结构损伤。 Cenetic算法为解决优化问题提供了强大的工具。通过适当选择操作员和参数，他们可以潜在地探索整个解决方案空间并达到全局最优。在这里，解决了Cenetic算法运算符和参数的设置问题，为在类似的损坏检测问题中选择它们提供了指导。研究了基于模态数据的五个基本功能的性能。另外，本文提出了使用损伤惩罚的方法，该方法可以令人满意地避免由于实验噪声或数值误差而导致的错误损伤检测。具有单个和多个损坏场景的三维空间框架结构提供了验证该方法的实验框架。在测得的自由度上以不同程度的不完整性测试该方法。结果表明，与常规优化方法相比，该方法可提供更为精确的解决方案。通过仅测量总自由度的6.3％，可以正确定位并量化三个同时损坏位置的场景。

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