Stochastic modelling and prediction of fatigue crack propagation using piecewise-deterministic Markov processes

Anis Ben Abdessalem, Romain Azaïs, Marie Touzet-Cortina, Anne Gégout-Petit and Monique Puiggali

Journal of Risk and Reliability, 2016, vol. 230, issue 4, 405-416

Abstract: Fatigue crack propagation is a stochastic phenomenon due to the inherent uncertainties originating from material properties, environmental conditions and cyclic mechanical loads. Stochastic processes thus offer an appropriate framework for modelling and predicting crack propagation. In this paper, fatigue crack growth is modelled and predicted by a piecewise-deterministic Markov process associated with deterministic crack laws. First, a regime-switching model is used to express the transition between the Paris regime and rapid propagation that occurs before failure. Both regimes of propagation are governed by a deterministic equation whose parameters are randomly selected in a finite state space. This one has been adjusted from real data available in the literature. The crack growth behaviour is well-captured and the transition between both regimes is well-estimated by a critical stress intensity factor range. The second purpose of our investigation deals with the prediction of the fatigue crack path and its variability based on measurements taken at the beginning of the propagation. The results show that our method based on this class of stochastic models associated with an updating method provides a reliable prediction and can be an efficient tool for safety analysis of structures in a large variety of engineering applications. In addition, the proposed strategy requires only little information to be effective and is not time-consuming.

Keywords: Fatigue crack propagation; uncertainties; stochastic processes; piecewise-deterministic Markov processes; regime-switching models; prediction (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:sae:risrel:v:230:y:2016:i:4:p:405-416

DOI: 10.1177/1748006X16651170

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