Probabilistic creep with the Wilshire–Cano–Stewart model

Abstract

Creep behavior is susceptible to uncertainty. Replicated creep data for steel can exhibit logarithm decades of uncertainty, which necessitates the development of probabilistic creep models for reliability‐based design. The deterministic Wilshire–Cano–Stewart (WCS) model is transformed into a probabilistic creep deformation, damage, and rupture prediction model via injecting the uncertainty of test conditions, pre‐existing damage, and material constants. The WCS model is calibrated to replicated quintuplicate creep data from a “single heat” of 304 stainless steel. The Markov chain Monte Carlo (MCMC) method with the Metropolis–Hastings (MH) algorithm is employed to introduce the sources of uncertainties into the model via calibrated probability distribution functions (pdfs). The probabilistic predictions encapsulate most of the experimental outliers across isostresses‐isotherms. Interpolative and extrapolative assessments reveal that minimum‐creep‐strain‐rate (MCSR) and stress rupture (SR) predictions are consistent across isotherms. The WCS probabilistic model captures the range of creep behavior of a single heat of material using short‐term data.