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Detection of Sparse Damages in Structures
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Structural and Fire Engineering.ORCID iD: 0000-0003-3731-7901
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Mathematical Science.ORCID iD: 0000-0001-7620-9386
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Structural and Fire Engineering.ORCID iD: 0000-0001-5187-2552
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Structural and Fire Engineering.ORCID iD: 0000-0002-0560-9355
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2019 (English)In: IABSE Symposium 2019: Towards a Resilent Built Environment - Risk and Asset Management, 2019Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
2019.
Keywords [en]
sparse damage, 𝑙2-norm, 𝑙1-norm, total variation, dictionary-based regularization, sensitivity
National Category
Engineering and Technology Mathematical Analysis Other Civil Engineering
Research subject
Mathematics; Structural Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-73335OAI: oai:DiVA.org:ltu-73335DiVA, id: diva2:1299660
Conference
IABSE Symposium 2019, Towards a Resilient Built Environment - Risk and Asset Management, March 27-29, 2019, Guimarães, Portugal
Available from: 2019-03-28 Created: 2019-03-28 Last updated: 2019-04-15

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Sabourova, NataliaGrip, NiklasOhlsson, UlfElfgren, Lennart

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