Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
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 (Autor:in) / Song, Xueli (Autor:in) / Wang, Fengdan (Autor:in) / Deng, Qingtian (Autor:in) / Li, Xinbo (Autor:in) / Xiao, Yuzhu (Autor:in)
Advances in Structural Engineering ; 26 ; 1565-1578
01.07.2023
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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