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    侯吉林

    • 副教授     博士生导师   硕士生导师
    • 性别:男
    • 毕业院校:哈尔滨工业大学
    • 学位:博士
    • 所在单位:土木工程系
    • 学科:结构工程
    • 办公地点:土木工程学院4号楼310
    • 电子邮箱:houjilin@dlut.edu.cn

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    Local Mass Addition and Data Fusion for Structural Damage Identification Using Approximate Models

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    论文类型:期刊论文

    第一作者:侯吉林

    通讯作者:Li, Zhenkun,Zhang, Qingxia,Jankowski, Lukasz,Zhang, Haibin

    发表时间:2020-10-01

    发表刊物:INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS

    卷号:20

    期号:11

    ISSN号:0219-4554

    关键字:Structural health monitoring (SHM); damage identification; adding mass; data fusion; objective function; modal assurance criterion (MAC)

    摘要: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 l(1)-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.