# Physics-based machine learning for fatigue lifetime prediction under non-uniform loading scenarios

arXiv: https://arxiv.org/abs/2503.05419 · HTML: https://mlsanity.com/paper/2503.05419/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 101 references that are in the map.
- First posted on arXiv 7 Mar 2025.

## Builds on (most cited first, 11 of 11)

- [Polyconvex anisotropic hyperelasticity with neural networks](https://mlsanity.com/paper/2106.14623/) (2021)
- [Viscoelasticty with physics-augmented neural networks: Model formulation and training methods without prescribed internal variables](https://mlsanity.com/paper/2401.14270/) (2024)
- [Modular machine learning-based elastoplasticity: generalization in the context of limited data](https://mlsanity.com/paper/2210.08343/) (2022)
- [Data-driven anisotropic finite viscoelasticity using neural ordinary differential equations](https://mlsanity.com/paper/2302.03598/) (2023)
- [Thermodynamics of learning physical phenomena](https://mlsanity.com/paper/2207.12749/) (2022)
- [Evolution TANN and the identification of internal variables and evolution equations in solid mechanics](https://mlsanity.com/paper/2209.13269/) (2022)
- [Accelerated high-cycle phase field fatigue predictions](https://mlsanity.com/paper/2304.02516/) (2023)
- [Phase field cohesive zone modeling for fatigue crack propagation in quasi-brittle materials](https://mlsanity.com/paper/2402.02421/) (2024)
- [A generalised phase field model for fatigue crack growth in elastic-plastic solids with an efficient monolithic solver](https://mlsanity.com/paper/2110.10425/) (2021)
- [A general framework for decomposing the phase field fracture driving force, particularised to a Drucker-Prager failure surface](https://mlsanity.com/paper/2208.10356/) (2022)
- [Exploring energy minimization to model strain localization as a strong discontinuity using Physics Informed Neural Networks](https://mlsanity.com/paper/2409.13241/) (2024)

## Abstract (arXiv)

Accurate lifetime prediction of structures subjected to cyclic loading is vital, especially in scenarios involving non-uniform loading histories where load sequencing critically influences structural durability. Addressing this complexity requires advanced modeling approaches capable of capturing the intricate relationship between loading sequences and fatigue lifetime. Traditional fatigue simulations are computationally prohibitive, necessitating more efficient methods. This study highlights the potential of physics-based machine learning (φML) to predict the fatigue lifetime of materials. Specifically, a FFNN is designed to embed physical constraints from experimental evidence directly into its architecture to enhance prediction accuracy. It is trained using numerical simulations generated by a physically based anisotropic continuum damage fatigue model. The model is calibrated and validated against experimental fatigue data of concrete cylinder specimens tested in uniaxial compression. The proposed approach demonstrates superior accuracy compared to purely data-driven neural networks, particularly in situations with limited training data, achieving realistic predictions of damage accumulation. Thus, a general algorithm is developed and successfully applied to predict fatigue lifetimes under complex loading scenarios with multiple loading ranges. Hereby, the φML model serves as a surrogate to capture damage evolution across load transitions. The φML based algorithm is subsequently employed to investigate the influence of multiple loading transitions on accumulated fatigue life, and its predictions align with trends observed in recent experimental studies. This work demonstrates φML as a promising technique for efficient and reliable fatigue life prediction in engineering structures, with possible integration into digital twin models for real-time assessment.