  A subcritical load on a disordered material can induce creep damage. The
creep rate in this case exhibits three temporal regimes viz. an initial
decelerating regime followed by a steady-state regime and a stage of
accelerating creep that ultimately leads to catastrophic breakdown. Due to the
statistical regularities in the creep rate, the time evolution of creep rate
has often been used to predict residual lifetime until catastrophic breakdown.
However, in disordered samples, these efforts met with limited success.
Nevertheless, it is clear that as the failure is approached, the damage become
increasingly spatially correlated, and the spatio-temporal patterns of acoustic
emission, which serve as a proxy for damage accumulation activity, are likely
to mirror such correlations. However, due to the high dimensionality of the
data and the complex nature of the correlations it is not straightforward to
identify the said correlations and thereby the precursory signals of failure.
Here we use supervised machine learning to estimate the remaining time to
failure of samples of disordered materials. The machine learning algorithm uses
as input the temporal signal provided by a mesoscale elastoplastic model for
the evolution of creep damage in disordered solids. Machine learning algorithms
are well-suited for assessing the proximity to failure from the time series of
the acoustic emissions of sheared samples. We show that materials are
relatively more predictable for higher disorder while are relatively less
predictable for larger system sizes. We find that machine learning predictions,
in the vast majority of cases, perform substantially better than other
prediction approaches proposed in the literature.
