Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.438529
Title: Structural health monitoring of GFRP sandwich beam structures
Author: Dawood, Tariq Ali
ISNI:       0000 0001 3418 2912
Awarding Body: University of Southampton
Current Institution: University of Southampton
Date of Award: 2006
Availability of Full Text:
Access from EThOS:
Access from Institution:
Abstract:
The use of GFRP sandwich materials is increasingly becoming popular. However, during the manufacturing process internal and external defects could occur. A vibration based NDE approach, using an SHM system, offers the possibility of examining the GFRP sandwich structure while it is in service. The research presented in this thesis highlights the various aspects of implementing this SHM based approach by studying and making improvements to individual components of the SHM system, namely an embedded sensor network and intelligent signal processing algorithms. A beam configuration was chosen as the test platform of choice to implement the SHM system for the GFRP sandwich structure. Considering the issue of embedding a sensor network within the GFRP sandwich structure, a modified vacuum infusion manufacturing technique was conceived which enabled a small network of optical FBG strain sensors to be successfully embedded between the skin and core of the GFRP sandwich beam structure. The intelligent signal processing algorithm developed utilised the wavelet transform estimated Lipschitz exponent as a damage sensitive signal feature. It was first successfully demonstrated, in its spatially varying form, for characterising debonds in numerically simulated models of GFRP sandwich beams. Subsequent experimental investigation of manufactured GFRP sandwich beam specimens, containing embedded FBG strain sensors and subjected to random loading conditions, showed that key features in the singularity spectrum, which is the spread of Lipschitz exponents, and its associated moment generating function, not only allowed successful identification of a variety of defects, but they also enabled quantitative identification of their approximation position on the GFRP sandwich beam specimen.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.438529  DOI: Not available
Share: