Predicting Failure

Forecasting failure is the goal in diverse domains that include earthquake physics, materials science, nondestructive evaluation of materials and other engineering applications.  Due to the highly complex physics of material failure, the goal appears out of reach; however, recent advances in instrumentation sensitivity, instrument density and data analysis show promise toward forecasting failure times. In this work we show that we can predict frictional failure times (‘labquakes’) in laboratory shear experiments. This advance is made possible by applying Random Forest (RF) machine learning to the continuous time series recorded by a single accelerometer listening to the experiment.  The RF is trained applying a number of statistical data features over a time interval over which a number of labquakes occur. Remarkably, during testing we find that the RF predicts upcoming failure time immediately following a labquake, based only on a short time window of data—a ‘now ’ prediction.  The predicted time improves as failure is approached, as other data features add to prediction.  The approach should be portable to most or all failure problems. References & Related Material: B. Rouet-Leduc, C. Hulbert, N. Lubbers, K. Barros and P. A. Johnson, Learning the physics of failure, in preparation (2016).