Physics-based machine learning for fatigue lifetime prediction under non-uniform loading scenarios
It addresses fatigue prediction for engineering structures, offering a more efficient method than traditional simulations, but is incremental as it builds on existing physics-based and machine learning approaches.
This study tackled the problem of predicting fatigue lifetime in structures under non-uniform loading by developing a physics-based machine learning (φML) model that embeds physical constraints into a neural network, achieving superior accuracy compared to purely data-driven methods, especially with limited training data, and successfully predicting damage accumulation in complex loading scenarios.
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 ($φ$ML) 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 $φ$ML model serves as a surrogate to capture damage evolution across load transitions. The $φ$ML 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 $φ$ML 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.