Abstract This paper presents a modified Artificial Bee Colony algorithm for structural damage identification. Meanwhile, the effect of temperature variation is considered and the change of temperature will lead to the alteration of Young's modulus of material. A novel objective function is proposed as the combinations of the partial mode shape curvature data, alterations of natural frequencies, and a sparse penalty term. Such an objective is found to be sensitive to structural damage while not sensitive to environmental effects. On the other hand, To render the standard Artificial Bee Colony algorithm more powerful and robustness, two local search strategies are introduced into the employed and onlooker bee phase of the Artificial Bee Colony algorithm, respectively. Two numerical examples and a laboratory verification are employed to verify the efficiency and advantage of the proposed algorithm. The final results show that the present algorithm could yield more satisfactory identification results compared with other state-of-the-art evolutionary algorithms, even high-level noise and temperature variation are considered; and the proposed novel objective function is more sensitive to structural damages, compared with the traditional mode-shape-based objective function.

中文翻译：

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基于新目标函数的变温结构损伤识别改进人工蜂群算法

摘要 本文提出了一种改进的人工蜂群算法，用于结构损伤识别。同时考虑了温度变化的影响，温度的变化会导致材料杨氏模量的改变。作为部分模态曲率数据、自然频率的改变和稀疏惩罚项的组合，提出了一种新的目标函数。发现这样的目标对结构损坏敏感，而对环境影响不敏感。另一方面，为了使标准人工蜂群算法更加强大和鲁棒，在人工蜂群算法的使用阶段和旁观者阶段分别引入了两种局部搜索策略。两个数值例子和实验室验证被用来验证所提出算法的效率和优势。最终结果表明，即使考虑了高水平噪声和温度变化，本算法与其他最先进的进化算法相比，也能产生更令人满意的识别结果；与传统的基于模态形状的目标函数相比，所提出的新目标函数对结构损伤更敏感。