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Abstract
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The need for efficient and
low-cost techniques adequate for damage detection has become of great interest
in engineering applications where structural health monitoring (SHM) is
of paramount importance. Promising algorithms for SHM have to deliver
results with very low computational and response time requirements and be
trustworthy within a certain accuracy. Different algorithms (artificial neural
networks (ANN), response surface methodology (RSM), and optimization
techniques — gradient-based local search (GBLS) and nondominated sorting genetic
algorithms (NSGA-II)) are proposed to fill this research gap. The concept of a
surrogate model as a fast-executing model is also introduced. Because the objective
of this paper is to concentrate on viable techniques suitable for damage
detection using vibration methods with very low computational requirements,
surrogates are therefore employed to curtail the computational expense.
Particularly of interest among the proposed algorithms is RSM, the principle
of which has proved successful in the pharmaceuticals industry over the
years. However, RSM has not been so widely used in the field of structural
engineering for delamination detection. In this paper, we have demonstrated
that a fourth-order polynomial has the capability to detect delaminations in
composite structures. In order to reduce the size of training data required to
solve the inverse problem by the proposed algorithms, the idea of a suitable
design space is brought to the limelight as the combination of all possible
simulations that one is concerned about. Since the overall sum of design
space is usually prohibitively large, we have used K-means clustering to
effectively achieve this. This research concerns the application of ANN, RSM, and
optimization techniques for delamination detection using changes in natural
frequencies before and after damage. Efficiencies of algorithms (ANN, GBLS, and
NSGA-II) are compared with the developed RSM models in terms of the
accuracy of delamination detection and response time requirements. The
methods have been shown to compete effectively for delamination detection
and are accurate in detecting the size and locations of delaminations at
midplanes. RSM has a unique feature in that it produces models with a small
training dataset requirement and also generates mathematical models that
are easy to interpret and implement. The optimization techniques, when
integrated with surrogate models, require small training sets clustered through
the entire design space. ANN, however, requires large training datasets to
achieve its results. As such, the potential of these algorithms as tools for
on-board damage detection when integrated into a SHM system is successfully
demonstrated. |
Keywords
vibrations, delaminations, ANN, K-means clustering, surrogates, RSM,
optimization techniques
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Milestones
Received: 1 November 2012
Accepted: 13 January 2013
Published: 18 November 2013
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