A STUDY ON FEMU: INFLUENCE OF SELECTION OF EXPERIMENTS ON RESULTS FOR ABS-M30 MATERIAL

This paper examines the effect of experiments used to identify material parameters of a more complex material model (12 material parameters). The set of experiments includes tensile tests and indentation tests with different loading conditions at 4 different temperatures (a total of 14 experiments) for the ABS-M30 material. The behaviour of the material was simulated using Anand's material model, and the Finite Element Model Updating approach was used to identify the material parameters. The parameters are solved for 3 variants: identification from indentation tests, identification from tensile tests, identification from all experiments. For the first two variants, the remaining experiments are used to verify. Finally, all results are compared.

Finite element model updating for the Tsing Ma Bridge tower based on surrogate models

This paper investigates the efficiency of three surrogate model-based dynamic finite element model updating methods, the response surface, the support vector regression, and the radial basis function neural network, using the engineering background of the Tsing Ma Bridge tower. The influences of two different sampling methods (central composite design sampling and Box–Behnken design sampling) on the model updating results are also assessed. It was deduced that the impact of the surrogate model type on the updating results is not significant. More precisely, the models updated using the response surface method and the support vector regression method are similar in terms of reproducing the dynamic characteristics of the physical truth. However, the effects of the employed sampling method on the model updating results are significant as the model updating quality using the central composite design sampling method is higher than that using the Box–Behnken design sampling method in some considered cases.