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arXiv · 2603.16419

Early Prediction of Creep Failure via Bayesian Inference of Evolving Barriers

Juan Carlos Verano-Espitia·Tero Mäkinen·Mikko J. Alava·Jérôme Weiss
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Abstract

Creep under a sustained load can persist for long times yet culminate in abrupt yielding or rupture, implying a finite lifetime even when the material appears solid. Here, we formulate lifetime prediction as Bayesian inference over an evolving activation-energy landscape. A time-dependent distribution of activation barriers controls deformation: stress lowers barriers, while irreversible rearrangements deplete the weakest sites and reshape the low-barrier tail. Using early-time acoustic emission data, Bayesian inference estimates the evolving barrier statistics in each sample and yields posterior predictive distributions for the time-to-failure. This approach provides online uncertainty-aware lifetime forecasts -- already at around 10~\% of the sample lifetime -- that link microscopic barrier evolution to macroscopic creep dynamics.

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Juan Carlos Verano-Espitia, Tero Mäkinen, Mikko J. Alava, Jérôme Weiss. 2026-03-17. Early Prediction of Creep Failure via Bayesian Inference of Evolving Barriers. https://arxiv.org/abs/2603.16419

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