Early Prediction of Creep Failure via Bayesian Inference of Evolving Barriers

TL;DR

This paper introduces a Bayesian inference framework to predict creep failure times by analyzing evolving activation barriers, enabling early and uncertainty-aware lifetime forecasts from acoustic emission data.

Contribution

It develops a novel Bayesian approach to model and infer the evolving energy landscape governing creep, providing early predictions of failure.

Findings

Early predictions at about 10% of the lifetime are feasible.

The method links microscopic barrier evolution to macroscopic creep behavior.

Posterior distributions quantify uncertainty in lifetime forecasts.

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.