Comparative studies on structural damage detection using L_p norm regularisation
by Zhenguang Yue; Zepeng Chen; Ling Yu
International Journal of Lifecycle Performance Engineering (IJLCPE), Vol. 3, No. 2, 2019

Abstract: In order to get sparse solution to the structural damage detection (SDD) problem, a new SDD method is proposed based on the finite element model updating technique through combining sensitivity analysis with the L_p (0 < p < 1) norm regularisation. Compared with the classical Tikhonov regularisation, the L_p norm regularisation method can avoid over-smoothness, which is a main difficulty for the Tikhonov regularisation, and its solutions are also more accurate than that by the L₁ norm regularisation, another widely used sparse regularisation method. Moreover, the effects of different p-values on SDD results are also studied and discussed. Numerical simulations on a cantilever beam and a simple supported beam, and all the laboratory experiments on a cantilever beam under different assumed damage cases verify the effectiveness of the proposed method. The SDD results show that a good sparse solution is assured via the L_p norm regularisation especially when the value of p ranges from 0.1 to 0.5. In contrast, its accuracy shows a slightly decline when the value of p changes from 0.5 to 1.0. Finally, the results also show that p = 1/2 is the best choice for SDD when using L_p norm regularisation.

Online publication date: Wed, 26-Jun-2019

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Lifecycle Performance Engineering (IJLCPE):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com