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Description
Accurate lifetime prediction of concrete structures under fatigue loading is vital, particularly in scenarios involving nonuniform 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 life [1,2]. Traditional high-cycle fatigue simulations are computationally prohibitive, necessitating more efficient methods. This contribution explores the potential of physics-informed machine learning to predict the fatigue lifetime of high-strength concrete, explicitly considering the effects of loading sequences in nonuniform loading scenarios [3,4]. A deep neural network was trained using numerical simulations generated by a physically-based anisotropic continuum damage fatigue model of concrete that was calibrated and validated against experimental fatigue data of cylinder specimens tested in uniaxial compression [5]. The simulations used for training quantified the effects of load sequences at two different amplitude levels. The deep neural network incorporates physical constraints derived from experimental evidence into the loss function of the neural network to improve its prediction accuracy, along with initial and boundary conditions. The proposed approach demonstrates superior accuracy compared to purely data-driven neural networks, achieving realistic predictions of damage accumulation. Furthermore, the model has been successfully applied to predict fatigue lifetimes under complex loading scenarios with three to five amplitude variables, serving as a surrogate model to estimate damage evolution across loading jumps. This work emphasizes the potential of physics-based neural networks as a promising technique for efficient and reliable fatigue life prediction of concrete structures susceptible to fatigue.
References:
[1] A. Baktheer, E. Martínez-Pañeda, F. Aldakheel, Phase field cohesive zone modeling for fatigue crack propagation in quasi-brittle materials, Comput. Methods Appl. Mech. Eng. 422 (2024) 116834. https://doi.org/10.1016/j.cma.2024.116834.
[2] A. Baktheer, C. Goralski, J. Hegger, R. Chudoba, Stress configuration-based classification of current research on fatigue of reinforced and prestressed concrete, Struct. Concr. 25 (2024) 1765–1781. https://doi.org/10.1002/suco.202300667.
[3] F. Aldakheel, R. Satari, P. Wriggers, Feed-Forward Neural Networks for Failure Mechanics Problems, Appl. Sci. 11 (2021). https://doi.org/10.3390/app11146483.
[4] A. Tragoudas, M. Alloisio, E.S. Elsayed, T.C. Gasser, F. Aldakheel, An enhanced deep learning approach for vascular wall fracture analysis, Arch. Appl. Mech. 94 (2024) 2519–2532. https://doi.org/10.1007/s00419-024-02589-3.
[5] A. Alliche, Damage model for fatigue loading of concrete, Int. J. Fatigue 26 (2004) 915–921. https://doi.org/10.1016/j.ijfatigue.2004.02.006.
