  An accurate and efficient simulation of the hysteretic behavior of materials
and components is essential for structural analysis. The surrogate model based
on neural networks shows significant potential in balancing efficiency and
accuracy. However, its serial information flow and prediction based on
single-level features adversely affect the network performance. Therefore, a
weighted stacked pyramid neural network architecture is proposed herein. This
network establishes a pyramid architecture by introducing multi-level shortcuts
to integrate features directly in the output module. In addition, a weighted
stacked strategy is proposed to enhance the conventional feature fusion method.
Subsequently, the redesigned architectures are compared with other commonly
used network architectures. Results show that the redesigned architectures
outperform the alternatives in 87.5% of cases. Meanwhile, the long and
short-term memory abilities of different basic network architectures are
analyzed through a specially designed experiment, which could provide valuable
suggestions for network selection.
