| Summary: | As a key material in high-temperature gas-cooled reactors (HTGR), nuclear graphite is a type of quasi-brittle material with complex damage mechanisms. However, it is difficult to characterise the damage properties of nuclear graphite under complex stress states using conventional testing approaches or inverse methods. In this study, a hybrid identification method based on 8-node quadrilateral element digital image correlation (Q8-DIC), a double iterative finite element model updating (FEMU) technique, and artificial neural networks (ANNs) is presented to characterise the damage properties of IG11 graphite material under complex stress states. First, this method is verified using simulated tests on a ring under diametrical compression, then applied to actual mechanical testing of graphite material to evaluate its damage properties. Finally, factors affecting the evolution of damage are discussed. The results indicate that the first principal strain has the most significant effect on the damage evolution of the graphite material. Keywords: Nuclear graphite, Damage, Complex stress states, Inverse identification, Artificial neural networks
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