Cohesive Crack Propagation in a Random Elastic Medium
Bruggi M., Casciati S., and Faravelli L. (2008). “Cohesive crack propagation in a random elastic medium”. Probabilistic Engineering Mechanics, 23(1), 23-35. ISSN: 0266-8920.
DATA E LUOGO DI PUBBLICAZIONE: January 2008; Elsevier Sci Ltd, Kidlington, Oxford OX5 1GB, Oxon, England.
ABSTRACT. The issue of generating non-Gaussian, multivariate and correlated random fields, while preserving the... more
ABSTRACT. The issue of generating non-Gaussian, multivariate and correlated random fields, while preserving the internal auto-correlation structure of each single-parameter field, is discussed with reference to the problem of cohesive crack propagation. Three different fields are introduced to model the spatial variability of the Young modulus, the tensile strength of the material, and the fracture energy, respectively. Within a finite-element context, the crack-propagation phenomenon is analyzed by coupling a Monte Carlo simulation scheme with an iterative solution algorithm based on a truly-mixed variational formulation which is derived from the Hellinger–Reissner principle. The selected approach presents the advantage of exploiting the finite-element technology without the need to introduce additional modes to model the displacement discontinuity along the crack boundaries. Furthermore, the accuracy of the stress estimate pursued by the truly-mixed approach is highly desirable, the direction of crack propagation being determined on the basis of the principal stress criterion. The numerical example of a plain concrete beam with initial crack under a three-point bending test is considered. The statistics of the response is analyzed in terms of peak load and load–mid deflection curves, in order to investigate the effects of the uncertainties on both the carrying capacity and the post-peak behaviour. A sensitivity analysis is preliminarily performed and its results emphasize the negative effects of not accounting for the auto-correlation structure of each random field. A probabilistic method is then applied to enforce the auto-correlation without significantly altering the target marginal distributions. The novelty of the proposed approach with respect to other methods found in the literature consists of not requiring the a priori knowledge of the global correlation structure of the multivariate random field.
KEYWORDS: Multivariate non-Gaussian random fields; Auto-correlation; Cohesive crack propagation; Truly-mixed finite element method; Monte Carlo simulations
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Seen by: and 14 morePhD Thesis - Continuous and Discrete Properties of Stochastic Processes
by Wai Ha Lee
My PhD thesis, "Continuous and Discrete Properties of Stochastic Processes". Policies regarding sharing and copying (and another copy of the full text) are available at the Nottingham eTheses website, http://etheses.nottingham.ac.uk/1194
Keywords:
continuous stable distribution, Gaussian distribution, discrete stable distribution, Poisson distribution, Fano factor, fracal properties, closed-form stable distributions, doubly stochastic Poisson transform, doubly stochastic Gaussian transform, Fokker-Planck equation, phase screen model, binomial, negative binomial, crossing statistics, inter-event density, persistence
This thesis considers the interplay between the continuous and discrete properties of random stochastic processes. It... more This thesis considers the interplay between the continuous and discrete properties of random stochastic processes. It is shown that the special cases of the one-sided Lévy-stable distributions can be connected to the class of discrete-stable distributions through a doubly-stochastic Poisson transform. This facilitates the creation of a one-sided stable process for which the N-fold statistics can be factorised explicitly. The evolution of the probability density functions is found through a Fokker-Planck style equation which is of the integro-differential type and contains non-local effects which are different for those postulated for a symmetric-stable process, or indeed the Gaussian process. Using the same Poisson transform interrelationship, an exact method for generating discrete-stable variates is found. It has already been shown that discrete-stable distributions occur in the crossing statistics of continuous processes whose autocorrelation exhibits fractal properties. The statistical properties of a nonlinear filter analogue of a phase-screen model are calculated, and the level crossings of the intensity analysed. It is found that rather than being Poisson, the distribution of the number of crossings over a long integration time is either binomial or negative binomial, depending solely on the Fano factor. The asymptotic properties of the inter-event density of the process are found to be accurately approximated by a function of the Fano factor and the mean of the crossings alone.
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Seen by: and 9 moreContinuous and discrete stable processes
by Wai Ha Lee
Co-Authored with Keith Hopcraft and Eric Jakeman, published in Physical Review E vol. 77, article 011109
The one-sided Lévy-stable probability densities and the discrete-stable distributions form a doubly stochastic Poisson... more The one-sided Lévy-stable probability densities and the discrete-stable distributions form a doubly stochastic Poisson transform pair. This relationship facilitates the formulation of a class of continuous-stable stochastic processes.
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Seen by:MMath Dissertation - Statistical Stability and the WWW
by Wai Ha Lee
MMath dissertation produced whilst studying Mathematics with Engineering at the University of Nottingham, supervised by Dr. Keith Hopcraft. Submitted in April 2005 and awarded a 90% grade.
A class of networks which are found in real-life are the so-called ‘scale-free’ networks, for which p(n) ~ 1/n^v,... more
A class of networks which are found in real-life are the so-called ‘scale-free’ networks, for which p(n) ~ 1/n^v, where p(n) is the ‘order distribution’, giving the probability that any given node has exactly n links connecting it to another node, and v is known as the ‘index’ of the distribution. In some of these networks, an outer scale is present, whereby p(n) decays exponentially instead of algebraically for n >> 1 – this ‘outer scale’ has the effect of making all the moments of the distribution finite. The sum of N scaled discrete variables each with order distribution p(n) will always converge to a Poisson distribution as N tends to infinity – the slowest convergence occurring when the index v is equal to two.
One can apply a ‘mean field’ approximation to the distribution, giving p(x), where x can take any non-negative value, as opposed to the integer values of n in p(n) – taking this approximation does not significantly alter the convergence properties, although the distribution of the sum now converges to a delta function. If p(x) is symmetric and extended to the entire x axis, but still exhibits scaled power law characteristics, the convergence is to a Gaussian distribution, the slowest convergence occurring when v = 3. The rate of convergence to the limiting distributions for scaled power-law distributions is found to have the same form, regardless of the model chosen or limiting distribution, as long as an outer scale is applied.
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