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Area of Science:

  • Materials Science
  • Physics
  • Data Science

Background:

  • Predicting material failure in disordered solids under stress is critical.
  • Avalanche dynamics in fracture processes exhibit universal critical behavior.
  • Identifying precursors to catastrophic failure is a significant challenge.

Purpose of the Study:

  • To develop a machine learning approach for predicting failure time in disordered solids.
  • To analyze avalanche size and energy burst time series for predictive signals.
  • To understand the role of different time series features in failure prediction.

Main Methods:

  • Utilized a fiber bundle model for disordered solids.
  • Recorded and analyzed time series of avalanche sizes and energy bursts.
  • Applied supervised machine learning techniques to predict time to failure.
  • Investigated the impact of varying interaction range and disorder strength on failure modes.

Main Results:

  • Supervised machine learning successfully predicts time to failure from avalanche time series.
  • Inequality measures of avalanche time series are crucial for imminent failure prediction, particularly with imperfect training data.
  • Predictability varies with interaction range and disorder strength, influencing failure modes (brittle/quasibrittle, nucleation/percolation).
  • Optimal prediction effectiveness is observed as failure transitions to the quasibrittle mode.

Conclusions:

  • Machine learning, specifically using inequality measures, offers a robust method for predicting catastrophic failure in disordered solids.
  • Understanding feature importance variations in machine learning models is key to improving prediction accuracy.
  • The study highlights the link between material properties, failure modes, and the predictability of breakdown events.