This article introduces a probabilistic fatigue model that combines the principles of the weakest link theory with the highly-stressed volume (HSV) or highly-stressed surface area (HSSA) concepts to estimate stress-life (P-S-N) curves for both unnotched (smooth) and notched large-scale specimens, for any prescribed failure probability. By incorporating probabilistic models, P-S-N curves corresponding to lower probabilities of failure can be obtained. The integration of HSV or HSSA with the weakest link principle enhances the estimation of fatigue strength for real elements or components, which often require testing with reduced-size specimens. The proposed model is verified by experimental fatigue data from the literature, specifically involving unnotched and notched cylindrical specimens subjected to different constant amplitude loadings. The results demonstrate the robustness and effectiveness of the model in estimating characteristic P-S-N curves at lower probabilities of failure.

A probabilistic stress-life model supported by weakest link principle and highly-stressed volume/surface area concepts

Benasciutti D.
Ultimo
2024

Abstract

This article introduces a probabilistic fatigue model that combines the principles of the weakest link theory with the highly-stressed volume (HSV) or highly-stressed surface area (HSSA) concepts to estimate stress-life (P-S-N) curves for both unnotched (smooth) and notched large-scale specimens, for any prescribed failure probability. By incorporating probabilistic models, P-S-N curves corresponding to lower probabilities of failure can be obtained. The integration of HSV or HSSA with the weakest link principle enhances the estimation of fatigue strength for real elements or components, which often require testing with reduced-size specimens. The proposed model is verified by experimental fatigue data from the literature, specifically involving unnotched and notched cylindrical specimens subjected to different constant amplitude loadings. The results demonstrate the robustness and effectiveness of the model in estimating characteristic P-S-N curves at lower probabilities of failure.
2024
Marques, J. M. E.; Mzourek, M.; Papuga, J.; Ruzicka, M.; Benasciutti, D.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/2528031
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