Simulation study on damage localization of a beam using evidence theory

Fei, Qingguo; Li, Aiqun; Han, Xiaolin

doi:10.1016/j.proeng.2009.06.034

Cited by 7 publications

(5 citation statements)

References 6 publications

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“…When multiple small damages exist in the target structure, to detect the structural damage will be much more difficult. Techniques such as the data fusion approach (Fei et al, 2009) can be adopted to improve the accuracy of the identification results.…”

Section: Numerical Simulationsmentioning

confidence: 99%

Reformulation of elemental modal strain energy method based on strain modes for structural damage detection

Zhou

Rui

et al. 2016

Advances in Structural Engineering

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Structural damage detection method based on modal strain energy usually requires information at every degree of freedom. Due to the limited number of sensors and the difficulty in measuring rotational vibration, modal expansion is often adopted to match the degrees of freedom between the analytical and experimental models, which introduces errors. A novel modal strain energy–based structural damage detection approach is proposed in which the elemental modal strain energy is reformulated with strain modes. The method is introduced on an Euler–Bernoulli beam with uniform cross section. Only strain data are adopted in the newly proposed method and no rotational information of the structure is required. The numerical simulations and experimental validations are conducted to demonstrate the effectiveness of the proposed method. The results show that the proposed method has a better performance than the modal strain energy–based structural damage detection approach with displacement mode and modal expansion technique.

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Section: Numerical Simulationsmentioning

confidence: 99%

Reformulation of elemental modal strain energy method based on strain modes for structural damage detection

Zhou

Rui

et al. 2016

Advances in Structural Engineering

Self Cite

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“…Due to limitations of the single damage index method, many scholars introduce data fusion into structural damage identification to improve the identification accuracy [15][16][17][18][19][20]. Grande et al [21,22] used the D-S evidence theory to realize beam structural damage based on the classical index of flexibility matrix and modal strain energy.…”

Section: Introductionmentioning

confidence: 99%

Corrosion Damage Identification of Towering Steel Headframe Based on Parametric Modeling and Data Fusion

Zhang

Chang

et al. 2022

Buildings

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The mine hoisting headframe is a typical towering truss structure, and its damage is complicated after long-term service, which affects the safety of the structure. To achieve rapid identification of corrosion damage on a towering steel headframe, the degradation law of headframe under different damage was analyzed via parametric modeling method and verified via field measurement. The results show that the influence range of element corrosion damage is localized, and a 50% corrosion rate of the bottom column will lead to a collapse risk of the headframe. After that, the single index method, of superimposed curvature mode difference and modal flexibility difference curvature, is used to identify corrosion damage of headframe column. The results indicate that the single index method is susceptible to interference in the undamaged position and leads to error identification. The improved multi-index data fusion damage identification method based on D-S evidence matrix is proposed, through which the multi-position damages in headframe structure can be identified accurately. Compared to the one-stage fusion method, the improved two-stage fusion method is more robust, with an increase of 42.9% in identification accuracy, and a reduction of 75% in misjudgment.

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“…D-S theory and cloud model are both effective ways to deal with uncertainty, and have been widely used in mechanical field, electronic field and medical field. In the field of structural damage identification, Qingguo Fei [11] used the modal strain energy change ratio by model shapes of different orders to locate the damage element of a fix-end beam, then applied the D-S theory to fuse the results of different orders. Hui Li [12] proposed a damage identification method based on the combination of ratification neural network, D-S evidence theory and the Shannon entropy and used a three-dimensional finite element model of the Binzhou Yellow River Highway Bridge to verify the accuracy of the method.…”

Section: Introductionmentioning

confidence: 99%

Structural damage detection based on cloud model and Dempster-Shafer evidence theory

Liang¹,

Guo²,

Zhou³

et al. 2018

J. vibroeng.

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Cloud model and D-S theory have been widely used in uncertainty reasoning. Meanwhile, modal strain energy and Inner Product Vector are also utilized as damage-sensitive features to detect structural damage. In this paper, a new structural damage identification approach is proposed based on Dempster-Shafer theory and cloud model. Cloud models were created to make uncertainty reasoning in damage structures by modal strain energy and the Inner Product Vector of acceleration. Then the results of the two methods were combined by using the Dempster-Shafer theory. Due to the classical D-S theory involves counter -intuitive behavious when the high conflicting evidences exists, the distance function was introduced to correct the conflict factor and combine the evidences. Moreover, a model of simple beam was created to verify the feasibility and accuracy for the single-damage and the multiple-damage. The effects of noise on damage detection were investigated simultaneously. The results show that the method has strong anti-noise ability and high accuracy.

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Simulation study on damage localization of a beam using evidence theory

Cited by 7 publications

References 6 publications

Reformulation of elemental modal strain energy method based on strain modes for structural damage detection

Reformulation of elemental modal strain energy method based on strain modes for structural damage detection

Corrosion Damage Identification of Towering Steel Headframe Based on Parametric Modeling and Data Fusion

Structural damage detection based on cloud model and Dempster-Shafer evidence theory

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