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The conventional group test method for obtaining probabilistic S- N curve is a time-consuming and expensive test program. This study aims to present an alternative method for determining the probabilistic S- N curve of material based on its static strength data. Firstly, the probabilistic mapping relationship between the static strength and fatigue life of material is elaborated. Subsequently, the Weibull distribution is utilized to model the static strength and fatigue life data of material, and the correlation between the distribution parameters of them is investigated. Finally, an alternative method for determining the probabilistic S- N curve of material is proposed, and the experimental data of carbon steels and composite laminates are applied to verify its validity. The results show that the proposed method is capable of determining the probabilistic S- N curve of material based on its static strength data, which can significantly save test time and reduce test cost. In engineering applications, when the parameters of static strength distribution and the median S- N curve are known, the probabilistic S- N curve with any given survival probability can be determined through a unified analytical expression.

The fatigue life of the materials is significantly reduced under non-proportional loading. In this study, the factors affecting additional hardening are explored, and a hardening function is proposed. Firstly, the stress and strain states of the specimen under multiaxial loading are analyzed, and the deficiencies of the equivalent strain models are discussed. Secondly, the factors affecting the additional hardening are analyzed from both stress and strain perspectives, and the effect of phase differences on fatigue life is investigated. The stress on the critical plane is considered to reflect its effect on crack initiation and growth. An improved multiaxial low-cycle fatigue life prediction model is developed based on the equivalent strain approach. Finally, experimental data from five metals are used to verify the established model and are compared with existing classical models. The results show that the proposed model has good accuracy.