lnu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
A methodological approach towards evaluating structural damage severity using 1D CNNs
Leeds University, UK.
Leeds University, UK.ORCID iD: 0000-0002-6243-052X
Linnaeus University, Faculty of Technology, Department of Building Technology.ORCID iD: 0000-0003-0530-9552
Iowa State University, USA.ORCID iD: 0000-0003-0221-7126
Show others and affiliations
2021 (English)In: Structures, E-ISSN 2352-0124, Vol. 34, p. 4435-4446Article in journal (Refereed) Published
Abstract [en]

Evaluating the severity of structural damage is a critical component of Structural Health Monitoring (SHM). Convolutional Neural Networks (CNNs) have been used before to detect structural damage and evaluate its severity by utilising only raw vibration data. However, these vibration-based CNN applications were limited to discrete user-defined levels of damage. To provide a more accurate representation of structural damage, this paper aims to design and validate a framework for evaluating structural damage severity within a continuous range of damage levels, using 1D CNNs and distributed raw acceleration data. To this purpose, a simple Finite Element (FE) cantilever model with non-rigid rotational spring support was adopted. Damage was simulated at the support as reduction of the rotational spring stiffness. The performance of the proposed framework was assessed under different excitation scenarios and data pre-processing techniques. The results demonstrate the ability of 1D CNNs to evaluate damage severity with high accuracy. By estimating the reduced value of the rotational spring stiffness, the proposed framework can also be used towards FE model updating in parallel with damage severity evaluation.

Place, publisher, year, edition, pages
Elsevier, 2021. Vol. 34, p. 4435-4446
Keywords [en]
Safety, Risk, Reliability and Quality, Building and Construction, Architecture, Civil and Structural Engineering
National Category
Building Technologies
Research subject
Technology (byts ev till Engineering), Civil engineering
Identifiers
URN: urn:nbn:se:lnu:diva-107865DOI: 10.1016/j.istruc.2021.10.029ISI: 000712159900003Scopus ID: 2-s2.0-85117725982Local ID: 2021OAI: oai:DiVA.org:lnu-107865DiVA, id: diva2:1610439
Available from: 2021-11-11 Created: 2021-11-11 Last updated: 2023-05-02Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Abdeljaber, Osama

Search in DiVA

By author/editor
Nikitas, NikolaosAbdeljaber, OsamaAvci, OnurBocian, Mateusz
By organisation
Department of Building Technology
Building Technologies

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 62 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf