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Robust Autonomous Damage Detection and Assessment in Polymeric Composite Structures

[+] Author Affiliations
Ajay Kesavan, Sabu John, Henry Li

RMIT University - Bundoora, Bundoora, VIC, Australia

Israel Herszberg

Cooperative Research Centre for Advanced Composite Structures, Fishermans Bend, VIC, Australia

Paper No. SMASIS2008-403, pp. 67-78; 12 pages
  • ASME 2008 Conference on Smart Materials, Adaptive Structures and Intelligent Systems
  • Smart Materials, Adaptive Structures and Intelligent Systems, Volume 2
  • Ellicott City, Maryland, USA, October 28–30, 2008
  • Conference Sponsors: Aerospace Division
  • ISBN: 978-0-7918-4332-1 | eISBN: 978-0-7918-3839-6
  • Copyright © 2008 by ASME


This paper introduces the some of the experimental and analytical work behind the autonomous damage detection technique. The research study conducted here resulted in the development of a Structural Health Monitoring (SHM) system for a 2-D polymeric composite T-joint, used in maritime structures. Two methods of damage detection are discussed — A statistics-based outlier technique and one using Artificial Neural Networks (ANNs). The SHM using ANNs system was found to be capable of not only detecting the presence of multiple delaminations in a composite structure, but also capable of determining the location and extent of all the delaminations present in the T-joint structure, regardless of the load (angle and magnitude) acting on the structure. The system developed relies on the examination of the strain distribution of the structure under operational loading. Finally, on testing the SHM system developed with strain signatures of composite T-joint structures, subjected to variable loading, embedded with all possible damage configurations (including multiple damage scenarios), an overall damage (location & extent) prediction accuracy of 94.1% was achieved. These results are presented and discussed in detail in this paper.

Copyright © 2008 by ASME



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