In practical civil engineering, structural damage identification is difficult to implement due to the shortage of measured modal information and the influence of noise. Furthermore, typical damage identification methods generally rely on a precise Finite Element (FE) model of the monitored structure. Pointwise mass alterations of the structure can effectively improve the quantity and sensitivity of the measured data, while the data fusion methods can adequately utilize various kinds of data and identification results. This paper proposes a damage identification method that requires only approximate FE models and combines the advantages of pointwise mass additions and data fusion. First, an additional mass is placed at different positions throughout the structure to collect the dynamic response and obtain the corresponding modal information. The resulting relation between natural frequencies and the position of the added mass is sensitive to local damage, and it is thus utilized to form a new objective function based on the modal assurance criterion (MAC) and [Formula: see text]-based sparsity promotion. The proposed objective function is mostly insensitive to global structural parameters, but remains sensitive to local damage. Several approximate FE models are then established and separately used to identify the damage of the structure, and then the Dempster–Shafer method of data fusion is applied to fuse the results from all the approximate models. Finally, fractional data fusion is proposed to combine the results according to the parametric probability distribution of the approximate FE models, which allows the natural weight of each approximate model to be determined for the fusion process. Such an approach circumvents the need for a precise FE model, which is usually not easy to obtain in real application, and thus enhances the practical applicability of the proposed method, while maintaining the damage identification accuracy. The proposed approach is verified numerically and experimentally. Numerical simulations of a simply supported beam and a long-span bridge confirm that it can be used for damage identification, including a single damage and multiple damages, with a high accuracy. Finally, an experiment of a cantilever beam is successfully performed.

中文翻译：

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使用近似模型进行结构损伤识别的局部质量添加和数据融合

在实际土木工程中，由于测量模态信息的不足和噪声的影响，结构损伤识别难以实施。此外，典型的损伤识别方法通常依赖于被监测结构的精确有限元 (FE) 模型。结构的逐点质量变化可以有效提高测量数据的数量和灵敏度，而数据融合方法可以充分利用各种数据和识别结果。本文提出了一种只需要近似有限元模型的损伤识别方法，并结合了逐点质量添加和数据融合的优点。首先，在整个结构的不同位置放置一个附加质量块，以收集动态响应并获得相应的模态信息。由此产生的固有频率和附加质量位置之间的关系对局部损伤很敏感，因此它被用来形成一个基于模态置信度（MAC）和基于[公式：见正文]的稀疏性提升的新目标函数. 所提出的目标函数对全局结构参数大多不敏感，但对局部损伤仍然敏感。然后建立几个近似有限元模型，分别用于识别结构的损伤，然后应用数据融合的Dempster-Shafer方法融合所有近似模型的结果。最后提出分数数据融合，根据近似有限元模型的参数概率分布对结果进行融合，这允许为融合过程确定每个近似模型的自然权重。这种方法避免了对精确有限元模型的需要，这在实际应用中通常不容易获得，从而增强了所提出方法的实际适用性，同时保持了损伤识别的准确性。所提出的方法经过数值和实验验证。简支梁和大跨度桥梁的数值模拟证实，该方法可用于单次损伤和多次损伤的损伤识别，具有较高的精度。最后，成功进行了悬臂梁实验。从而增强了所提出方法的实际适用性，同时保持了损伤识别的准确性。所提出的方法经过数值和实验验证。简支梁和大跨度桥梁的数值模拟证实，该方法可用于单次损伤和多次损伤的损伤识别，具有较高的精度。最后，成功进行了悬臂梁实验。从而增强了所提出方法的实际适用性，同时保持了损伤识别的准确性。所提出的方法经过数值和实验验证。简支梁和大跨度桥梁的数值模拟证实，该方法可用于单次损伤和多次损伤的损伤识别，具有较高的精度。最后，成功进行了悬臂梁实验。