105,386 research outputs found
Extended Analysis of a Damage Prognosis Approach Based on Interval Arithmetic
In the previous work by the authors, an approach to damage prognosis which incorporates the effects of uncertainties has been proposed. This approach is based on the idea of integrating the laws for damage progression within the framework of interval arithmetic, a framework that naturally accommodates uncertainty. The modest purposes of this study are to extend/modify the approach in a small but significant manner and also to consider a more complex problem than the previous benchmark. The developments in the paper are illustrated through two case studies. The first case study revisits the benchmark of the initial paper - an isotropic finite plate under harmonic uniaxial loading, where the damage is assumed to be a central, mode I, through-crack. The damage propagation model for the cracked plate is the Paris-Erdogan law. The second example considers the growth of internal delaminations in composite plates subjected to cyclic compression. Under compressive loading, laminated composite plates experience repeated buckling-unloading of the delaminated layer with a consequent reduction in interlayer resistance. The state of stress near the delamination tip is of mixed mode I and II. A graphite-epoxy unidirectional specimen has been assumed here and the thin-film model of Chai and colleagues is used; the latter is a closed-form solution for the initial post-buckling and growth behaviour in the ideal case of a surface delamination in an infinitely thick plate. The delamination growth law considered is the one proposed by Kardomateas and co-workers. As the objective of the study is to examine the effects of uncertainties, the various models are considered within the framework of interval arithmetic; Monte Carlo analysis is also carried out to provide a basis for compariso
A Novelty Detection method to diagnose damage in structures: Anapplication to an Offshore Platform
Joshua Davis: Author of Spare Parts
Citation: K-State First (2016). Joshua Davis: Author of Spare Parts [Flier]. Manhattan, Kansas: K-State First.Flyer advertising Joshua Davis's author talk at Kansas State University
Novelty detection in a changing environment: A negative selection approach
In the recent past, there have been a number of engineering studies motivated by analogies with the human immune system. The immune system has provided a rich source of inspiration for pattern recognition, machine learning and data mining analyses. One of the properties of the immune system which proves particularly useful for novelty detection is that of self/non-self discrimination and this forms the basis of the negative selection algorithm which has previously been applied by other researchers to the problem of time-series novelty detection. The object of the current paper is to apply the negative selection algorithm to more general feature sets and also to consider the case of novelty detection where the normal condition set is significantly non- Gaussian or varies with operational or environmental condition
Curve fitting of mixed-mode isopachics
Recent work has focused on exploiting the observation that the stress-sum contours
(isopachics), obtained from TSA, in the vicinity of the tip take the form of a simple curve – the
cardioid. The analysis made use of the cardioid nature of the isopachics by deriving
expressions for the SIFs in terms of the cardioid area and the positions of certain tangents to
the curve. Both Genetic Algorithms (GAs) and Differential Evolution (DE) have also proved
successful for parameter estimation, but some of the curve-fits indicated that the cardioid form
was inappropriate for the base model, particularly for mixed-mode cracks. The effect of cracktip
interaction has been explored and shows this has a small effect on the cardioid form. New,
higher resolution infra-red detectors have become available since the original data was
collected, so the object of the current paper is to use new techniques to extract the cardioid
form and use a GA to perform the curve fitting
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