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On Two Damage Accumulation Models and Their Size Effects

Published online by Cambridge University Press:  14 July 2016

F. Ballani*
Affiliation:
TU Bergakademie Freiberg
D. Stoyan*
Affiliation:
TU Bergakademie Freiberg
S. Wolf*
Affiliation:
TU Bergakademie Freiberg
*
Postal address: Institute of Stochastics, TU Bergakademie Freiberg, Prüferstrasse 9, D-09596 Freiberg, Germany.
Postal address: Institute of Stochastics, TU Bergakademie Freiberg, Prüferstrasse 9, D-09596 Freiberg, Germany.
Postal address: Institute of Stochastics, TU Bergakademie Freiberg, Prüferstrasse 9, D-09596 Freiberg, Germany.
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Abstract

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Two cumulative damage models are considered, the inverse gamma process and a composed gamma process. They can be seen as ‘continuous’ analogues of Poisson and compound Poisson processes, respectively. For these models the first passage time distribution functions are derived. Inhomogeneous versions of these processes lead to models closely related to the Weibull failure model. All models show interesting size effects.

Type
Research Article
Copyright
Copyright © Applied Probability Trust 2007 

References

Applebaum, D. (2004). Lévy Processes and Stochastic Calculus. Cambridge University Press.CrossRefGoogle Scholar
Bažant, Z. P. and Planas, J. (1998). Fracture and Size Effect in Concrete and other Quasibrittle Materials. CRC Press, Washington, DC.Google Scholar
Çinlar, E. (1980). On a generalization of gamma processes. J. Appl. Prob. 17, 467480, 893.CrossRefGoogle Scholar
Daley, D. J. and Vere-Jones, D. (2003). An Introduction to the Theory of Point Processes, Vol. 1, 2nd edn. Springer, New York.Google Scholar
Dufresne, F., Gerber, H. U. and Shiu, E. S. W. (1991). Risk theory with the gamma process. ASTIN Bull. 22, 177192.CrossRefGoogle Scholar
Duxbury, P. M. and Leath, P. L. (1994). Exactly solvable models of material breakdown. Phys. Rev. B 49, 1267612686.CrossRefGoogle ScholarPubMed
Evans, M., Hastings, N. and Peacock, B. (1993). Statistical Distributions, 2nd edn. John Wiley, New York.Google Scholar
Ferguson, T. S. (1974). Prior distributions of spaces of probability measures. Ann. Statist. 2, 615629.CrossRefGoogle Scholar
Ferguson, T. S. and Klass, M. J. (1972). A representation of independent increment processes without Gaussian components. Ann. Math. Statist. 43, 16341643.CrossRefGoogle Scholar
Gautschi, W. (1998). The incomplete gamma functions since Tricomi. In Tricomi's Ideas and Contemporary Applied Mathematics (Atti dei Convegni Lincei 147), Accademia Nazionale dei Lincei, Roma, pp. 203237.Google Scholar
Jeulin, D. (1994). Random structure models for composite media and fracture statistics. In Advances in Mathematical Modelling of Composite Materials, ed. Markov, K., World Scientific, Singapore, pp. 239289.CrossRefGoogle Scholar
Kingman, J. F. C. (1993). Poisson Processes (Oxford Studies Prob. 3). Oxford University Press.Google Scholar
Krajcinovic, D. (1996). Damage Mechanics, Applied Mathematics and Mechanics. Elsevier, Amsterdam.Google Scholar
Mann, N. R., Schafer, R. E. and Singpurwalla, N. D. (1974). Methods for Statistical Analysis of Reliability and Life Data. John Wiley, New York.Google Scholar
Onar, A. and Padgett, W. J. (2000). Inverse Gaussian accelerated test models based on cumulative damage. J. Statist. Comput. Simul. 64, 233247.CrossRefGoogle Scholar
Park, C. and Padgett, W. J. (2005). New cumulative damage models for failure using stochastic processes as initial damage. IEEE Trans. Reliab. 54, 530540.CrossRefGoogle Scholar
Pompe, W. et al. (1985). Mechanical Properties of Brittle Materials, Modern Theories and Experimental Evidence. Elsevier, Amsterdam.Google Scholar
Rolski, T., Schmidli, H., Schmidt, H. and Teugels, J. (1999). Stochastic Processes for Insurance and Finance. John Wiley, New York.CrossRefGoogle Scholar
Sobczyk, K. (1987). Stochastic models for fatigue damage of materials. Adv. Appl. Prob. 19, 652673.CrossRefGoogle Scholar
Sobczyk, K. (1997). On cumulative Jump models for random deterioration processes. Ann. Univ. Maria Curie-Sklodowska, Lublin 15, 145157.Google Scholar
Sobczyk, K. and Spencer, B. F. (1992). Random Fatigue: From Data to Theory. Academic Press, Boston, MA.Google Scholar
Todinov, M. T. (2002). Statistics of defects in one-dimensional components. Comput. Mat. Sci. 24, 430442.CrossRefGoogle Scholar
Wolf, S., Wiegand, S., Stoyan, D. and Walther, H. B. (2005). The compressive strength of AAC – a statistical investigation. In Autoclaved Aerated Concrete. Innovation and Design, Taylor and Francis, London, pp. 287295.Google Scholar