Affiliation:
1. Lulea University of Technology, SE-971 87 Lulea, Sweden
2. School of Civil Engineering, Southeast University, Nanjing, China
3. University of Zagreb, Croatia
Abstract
<p>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 compressed sensing problems found its application in this field.</p><p>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, (I), 𝑙2- norm regularization was used to solve the ill-posed inverse problems, such as damage identification. However, using already well established, (II), 𝑙l-norm regularization or our proposed, (III), 𝑙l-norm total variation regularization and, (IV), 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.</p>
Publisher
International Association for Bridge and Structural Engineering (IABSE)
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Detection of Sparse Damages in Plates;IABSE Symposium, Wroclaw 2020: Synergy of Culture and Civil Engineering – History and Challenges;2020
2. Damage Detection in Structures – Examples;IABSE Symposium, Guimarães 2019: Towards a Resilient Built Environment Risk and Asset Management;2019