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Detection of Sparse Damages in Structures
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Byggkonstruktion och brand.ORCID-id: 0000-0003-3731-7901
Luleå tekniska universitet, Institutionen för teknikvetenskap och matematik, Matematiska vetenskaper.ORCID-id: 0000-0001-7620-9386
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Byggkonstruktion och brand.ORCID-id: 0000-0001-5187-2552
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Byggkonstruktion och brand.ORCID-id: 0000-0002-0560-9355
Vise andre og tillknytning
2019 (engelsk)Inngår i: IABSE Symposium 2019: Towards a Resilent Built Environment - Risk and Asset Management, 2019Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
2019.
Emneord [en]
sparse damage, 𝑙2-norm, 𝑙1-norm, total variation, dictionary-based regularization, sensitivity
HSV kategori
Forskningsprogram
Matematik; Byggkonstruktion
Identifikatorer
URN: urn:nbn:se:ltu:diva-73335OAI: oai:DiVA.org:ltu-73335DiVA, id: diva2:1299660
Konferanse
IABSE Symposium 2019, Towards a Resilient Built Environment - Risk and Asset Management, March 27-29, 2019, Guimarães, Portugal
Tilgjengelig fra: 2019-03-28 Laget: 2019-03-28 Sist oppdatert: 2019-04-15

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Sabourova, NataliaGrip, NiklasOhlsson, UlfElfgren, Lennart

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