Method for statistical evaluation of cumulative damage models applied to block loading

There is an abundance of nonlinear fatigue damage accumulation models but a lack of verification and comparison to experimental datasets. First, an extensive two-level block loading experimental fatigue dataset is reprocessed according to current standard practice. Next, four nonlinear damage accumulation models and the Palmgren-Miner linear damage rule are critically compared using a range of statistical metrics. For the considered dataset, the Palmgren-Miner rule consistently performs worst and the damage curve approach is found to perform significantly better than the other nonlinear models. This study shows that the current practice of performance verification of new fatigue damage accumulation models in literature is too limited which enables cherry-picking of verification datasets to improve perceived performance. Future fatigue damage accumulation models should be verified much more rigorously to both readily available and new experimental datasets.

Automated summary

This study finds that the current practice of verifying fatigue damage accumulation models is too limited, allowing cherry-picking of verification datasets to improve perceived performance. Future research should rigorously verify new fatigue damage accumulation models to both readily available and new experimental datasets.