A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
A fraction function regularization model for the structural damage identification
https://orcid.org/0000-0002-8775-423X
https://orcid.org/0000-0002-5595-6590
The conventional model updating based on sensitivity analysis generally employs l1-norm regularizer to characterize the sparsity of the structural damage. However, the l1-norm regularizer inevitably excessively penalizes the larger components in the damage parameter, which certainly causes the extra estimation bias of the damage parameter and reduces the damage identification accuracy. A fraction function regularizer not only well characterizes the sparsity, but also overcomes the excessive penalty drawback of the l1-norm regularizer. Based on this, a fraction function regularization model is proposed to improve the damage identification accuracy. Numerical and experimental results illustrate that the damage identification accuracy of the proposed model is averagely improved 4.96% and 3.68% than that of the l1 regularization one, the iteratively reweighted l1 regularization one and the elastic net one, respectively.
A fraction function regularization model for the structural damage identification
https://orcid.org/0000-0002-8775-423X
https://orcid.org/0000-0002-5595-6590
The conventional model updating based on sensitivity analysis generally employs l1-norm regularizer to characterize the sparsity of the structural damage. However, the l1-norm regularizer inevitably excessively penalizes the larger components in the damage parameter, which certainly causes the extra estimation bias of the damage parameter and reduces the damage identification accuracy. A fraction function regularizer not only well characterizes the sparsity, but also overcomes the excessive penalty drawback of the l1-norm regularizer. Based on this, a fraction function regularization model is proposed to improve the damage identification accuracy. Numerical and experimental results illustrate that the damage identification accuracy of the proposed model is averagely improved 4.96% and 3.68% than that of the l1 regularization one, the iteratively reweighted l1 regularization one and the elastic net one, respectively.
A fraction function regularization model for the structural damage identification
https://orcid.org/0000-0002-8775-423X
https://orcid.org/0000-0002-5595-6590
The conventional model updating based on sensitivity analysis generally employs l1-norm regularizer to characterize the sparsity of the structural damage. However, the l1-norm regularizer inevitably excessively penalizes the larger components in the damage parameter, which certainly causes the extra estimation bias of the damage parameter and reduces the damage identification accuracy. A fraction function regularizer not only well characterizes the sparsity, but also overcomes the excessive penalty drawback of the l1-norm regularizer. Based on this, a fraction function regularization model is proposed to improve the damage identification accuracy. Numerical and experimental results illustrate that the damage identification accuracy of the proposed model is averagely improved 4.96% and 3.68% than that of the l1 regularization one, the iteratively reweighted l1 regularization one and the elastic net one, respectively.
A fraction function regularization model for the structural damage identification
Li, Rongpeng (author) / Song, Xueli (author) / Wang, Fengdan (author) / Deng, Qingtian (author) / Li, Xinbo (author) / Xiao, Yuzhu (author)
Advances in Structural Engineering ; 26 ; 1565-1578
2023-07-01
Article (Journal)
Electronic Resource
English
A joint fraction function regularization model for the structural damage identification
| SAGE Publications | 2024
Structural damage detection using nonlinear parameter identification with Tikhonov regularization
| Wiley | 2007
Structural damage detection using nonlinear parameter identification with Tikhonov regularization
| Online Contents | 2007
Modified Tikhonov regularization in model updating for damage identification
| British Library Online Contents | 2012
Comparative studies on damage identification with Tikhonov regularization and sparse regularization
| Wiley | 2016