A fraction function regularization model for the structural damage identification

Li, Rongpeng; Song, Xueli; Wang, Fengdan; Deng, Qingtian; Li, Xinbo; Xiao, Yuzhu

doi:10.1177/13694332221151017

Cited by 3 publications

(1 citation statement)

References 43 publications

(54 reference statements)

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“…Firstly, in this numerical study, the L -curve criterion is applied to select the optimal sparsity regularization parameter β in damage scenario 1 for the joint fraction function regularization model (10), joint sparse regularization model (Li et al, 2023c) and fraction function regularization model (Li et al, 2023b), respectively Hou et al (2018b); Yao et al (2011). Then the selected parameter was substituted into the corresponding model.…”

Section: Numerical Studymentioning

confidence: 99%

A joint fraction function regularization model for the structural damage identification

Li,

Wang,

Jiang

et al. 2023

Advances in Structural Engineering

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The sparse regularization (SReg) model has been prove to be an effective tool for the structural damage identification. However, the SReg model overly penalizes the larger components in the damage parameter leading to extra estimation bias, and it ignores the similarity information among different measurements that is useful for improving the performance of damage identification. To further improve the accuracy of damage identification, this study proposes a joint fraction function regularization model by jointing multiple fraction function regularization models, where fraction function regularizers are utilized to overcome the excessive penalty drawback, and the similarity of different measurements is employed through a data fusion technique. The numerical and experimental study show that, compared with the previous SReg models, the damage identification errors of the proposed model are reduced by 4.15% and 2.12% on average, respectively.

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Section: Numerical Studymentioning

confidence: 99%

A joint fraction function regularization model for the structural damage identification

Li,

Wang,

Jiang

et al. 2023

Advances in Structural Engineering

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Damage Identification of Full-Scale Steel Truss Structure Based on Model Condensation and Mean-Value Normalization Regularization Techniques

Chen,

Zhang,

Yuan

2024

Advances in Civil Engineering

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Structural health monitoring and damage identification aim to detect the internal damage and evaluate the health conditions of the practical engineering structure, which has been the most popular research field for several decades. The sensitivity-based method incorporated with the regularization techniques is the classical and useful approach, and it can obtain accurate damage detection results. However, with the development of civil engineering structures, this classical method faces two problems: one is it is only applied to simple structures rather than full-scale structures, and second is the iterative calculation efficiency is lower. Therefore, aiming at these drawbacks, the two improvement strategies have been introduced to the original method for its enhancement in the application potential and computational efficiency. The proposed method has been verified based on two examples, i.e., a numerical steel truss with 144 elements and a full-scale experimental steel truss with 160 elements. The results prove that the proposed method has better efficiency and good application potential in the practical full-scale engineering structure.

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A Probabilistic Structural Damage Identification Method with a Generic Non-Convex Penalty

Li,

Yi,

Wang

et al. 2024

Mathematics

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Due to the advantage that the non-convex penalty accurately characterizes the sparsity of structural damage, various models based on non-convex penalties have been effectively utilized to the field of structural damage identification. However, these models generally ignore the influence of the uncertainty on the damage identification, which inevitably reduces the accuracy of damage identification. To improve the damage identification accuracy, a probabilistic structural damage identification method with a generic non-convex penalty is proposed, where the uncertainty corresponding to each mode is quantified using the separate Gaussian distribution. The proposed model is estimated via the iteratively reweighted least squares optimization algorithm according to the maximum likelihood principle. The numerical and experimental results illustrate that the proposed method improves the damage identification accuracy by 3.98% and 7.25% compared to the original model, respectively.

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A fraction function regularization model for the structural damage identification

Cited by 3 publications

References 43 publications

A joint fraction function regularization model for the structural damage identification

A joint fraction function regularization model for the structural damage identification

Damage Identification of Full-Scale Steel Truss Structure Based on Model Condensation and Mean-Value Normalization Regularization Techniques

A Probabilistic Structural Damage Identification Method with a Generic Non-Convex Penalty

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