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Structural damage detection based on iteratively reweighted l1 regularization algorithm

Structural damage detection based on iteratively reweighted l1 regularization algorithm Structural damage usually appears in a few sections or members only, which is sparse compared with the total elements of the entire structure. According to the sparse recovery theory, the recently developed damage detection methods employ the l1 regularization technique to exploit the sparsity condition of structural damage. However, in practice, the solution obtained by the l1 regularization is typically suboptimal. The l0 regularization technique outperforms the l1 regularization in various aspects for sparse recovery, whereas the associated nonconvex optimization problem is NP-hard and computationally infeasible. In this study, a damage detection method based on the iteratively reweighted l1 regularization algorithm is proposed. An iterative procedure is employed such that the nonconvex optimization problem of the l0 regularization can be efficiently solved through transforming it into a series of weighted l1 regularization problems. Experimental example demonstrates that the proposed damage detection method can accurately locate the sparse damage over a large number of elements. The advantage of the iteratively reweighted l1 regularization algorithm over the l1 regularization in damage detection is also demonstrated. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Advances in Structural Engineering SAGE

Structural damage detection based on iteratively reweighted l1 regularization algorithm

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References (51)

Publisher
SAGE
Copyright
© The Author(s) 2018
ISSN
1369-4332
eISSN
2048-4011
DOI
10.1177/1369433218817138
Publisher site
See Article on Publisher Site

Abstract

Structural damage usually appears in a few sections or members only, which is sparse compared with the total elements of the entire structure. According to the sparse recovery theory, the recently developed damage detection methods employ the l1 regularization technique to exploit the sparsity condition of structural damage. However, in practice, the solution obtained by the l1 regularization is typically suboptimal. The l0 regularization technique outperforms the l1 regularization in various aspects for sparse recovery, whereas the associated nonconvex optimization problem is NP-hard and computationally infeasible. In this study, a damage detection method based on the iteratively reweighted l1 regularization algorithm is proposed. An iterative procedure is employed such that the nonconvex optimization problem of the l0 regularization can be efficiently solved through transforming it into a series of weighted l1 regularization problems. Experimental example demonstrates that the proposed damage detection method can accurately locate the sparse damage over a large number of elements. The advantage of the iteratively reweighted l1 regularization algorithm over the l1 regularization in damage detection is also demonstrated.

Journal

Advances in Structural Engineering – SAGE

Published: Apr 1, 2019

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