Enhanced data-driven Damage Detection for Structural Health Monitoring Systems

Marwa Chaabane*, Ahmed Ben Hamida, Majdi Mansouri, Hazem Nounou, Mohamed Nounou

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In structural engineering, it is essential to monitor the operation condition of an aging structure. Thus, damage detection is widely used for structure monitoring. The aim of this work is to propose an adaptive kernel PLS based GLRT chart to improve the detection of damage in civil structural systems. The proposed technique aims to integrate the advantages of the adaptive nonlinear input-output model (kernel PLS) with those of GLRT chart. This technique will be tested using a simulated benchmark structure through the surveillance model variables. The technique based on adaptive representation is found to be more effective over the conventional technique.

Original languageEnglish
Title of host publication2020 International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728175133
DOIs
Publication statusPublished - Sept 2020
Externally publishedYes
Event5th International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2020 - Sfax, Tunisia
Duration: 2 Sept 20205 Sept 2020

Publication series

Name2020 International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2020

Conference

Conference5th International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2020
Country/TerritoryTunisia
CitySfax
Period2/09/205/09/20

Keywords

  • Adaptive
  • Damage Detection
  • GLRT
  • Kernel PLS
  • Structural Health Monitoring

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