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Detection of Sparse Damages in Structures
Structural damage is often a spatially sparse phenomenon, i.e. it occurs only in a small part of the structure. This property of damage has not been utilized in the field of structural damage identification until quite recently, when the sparsity-based regularization developed in the compressed sensing found its application in this field. In this paper we consider classical sensitivity-based finite element model updating combined with a regularization technique appropriate for the expected type of sparse damage. Traditionally (1) 𝑙2-norm regularization was used to solve the ill-posed inverse problems, such as damage identification. However, using (2) already well established 𝑙1-norm regularization or (3) our proposed 𝑙1-norm total variation regularization and (4) general dictionary-based regularization allows us to find damages with special spatial properties quite precisely using much fewer measurement locations than the number of possibly damaged elements of the structure. The validity of the proposed methods is demonstrated using simulations on a Kirchhoff plate model. The pros and cons of these methods are discussed. ; ISBN för värdpublikation: 978-1-5108-8445-8
Detection of Sparse Damages in Structures
Structural damage is often a spatially sparse phenomenon, i.e. it occurs only in a small part of the structure. This property of damage has not been utilized in the field of structural damage identification until quite recently, when the sparsity-based regularization developed in the compressed sensing found its application in this field. In this paper we consider classical sensitivity-based finite element model updating combined with a regularization technique appropriate for the expected type of sparse damage. Traditionally (1) 𝑙2-norm regularization was used to solve the ill-posed inverse problems, such as damage identification. However, using (2) already well established 𝑙1-norm regularization or (3) our proposed 𝑙1-norm total variation regularization and (4) general dictionary-based regularization allows us to find damages with special spatial properties quite precisely using much fewer measurement locations than the number of possibly damaged elements of the structure. The validity of the proposed methods is demonstrated using simulations on a Kirchhoff plate model. The pros and cons of these methods are discussed. ; ISBN för värdpublikation: 978-1-5108-8445-8
Detection of Sparse Damages in Structures
Sabourova, Natalia (author) / Grip, Niklas (author) / Ohlsson, Ulf (author) / Elfgren, Lennart (author) / Tu, Yongming (author) / Duvnjak, Ivan (author) / Damjanović, Domagoj (author)
2019-01-01
Conference paper
Electronic Resource
English
DETECTION OF SPARSE DAMAGES IN PLATES
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