No abstract

Vertical links are dissipative devices in form of beam‐column segments that are included in eccentrically braced frames (EBF). Studies related to different configurations of links have been presented for carbon steel elements. Moreover, stainless steel is a nonlinear metallic material with particular appeal for its structural use due to a considerable amount of capabilities such as corrosion, circular life‐cycle, strength, ductility and aesthetics. Despite the increase on the use of stainless steel for structural elements in construction, the behavior of this material for structural purposes is not described as carbon steel in all applications. The elastic stiffness, maximum load, shear deformation capacity as well as the energy dissipation in long links are generally affected by the defined geometrical parameters. Due to the material nonlinearity, this influence may be affected considerably. In this paper, a numerical model aimed at depicting the cyclic behavior of vertical short and long links subjected to seismic lateral loads is presented. The model accounts for the material nonlinearity, the geometrical nonlinearity and the detailed definition of boundary conditions from the link connection to the EBF.

The onset of localized necking under monotonic and non-monotonic loading can be well-predicted by the imperfection-based approach proposed by [1] (MK). However, a large number of virtual imperfections has to be investigated for an accurate necking prediction, making the MK approach computationally expensive and hence preventing the industrial application for full-scale vehicle models. To overcome these issues, a computationally efficient neural network (NN) model is proposed for replacing the MK model in the present work. An extended version of the MK model has been implemented into a User Material for an explicit crash solver. The model continuously computes the “distance to localized necking” as an important engineering quantity. Single shell element simulations are utilized for creating a comprehensive virtual test database for monotonic and non-monotonic loading for a 22MnB5 grade in an as-delivered state. A simple feed-forward NN model, featuring only one hidden layer, is trained and tested against the virtual data, where invariants of the stress and plastic strain tensors represent the input features of the NN and the “distance to necking” represents the output value of the NN. For comparison of the computational cost, the NN architecture has also been implemented in a User Material Routine for shell elements. The predictions of the NN and the MK model are in good agreement, where, due to the simple mathematical structure of the NN, the computational cost of the NN is significantly lower than for the MK implementation.