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An accurate and efficient simulation of the hysteretic behavior of materialsand components is essential for structural analysis. The surrogate model basedon neural networks shows significant potential in balancing efficiency andaccuracy. However, its serial information flow and prediction based onsingle-level features adversely affect the network performance. Therefore, aweighted stacked pyramid neural network architecture is proposed herein. Thisnetwork establishes a pyramid architecture by introducing multi-level shortcutsto integrate features directly in the output module. In addition, a weightedstacked strategy is proposed to enhance the conventional feature fusion method.Subsequently, the redesigned architectures are compared with other commonlyused network architectures. Results show that the redesigned architecturesoutperform the alternatives in 87.5% of cases. Meanwhile, the long andshort-term memory abilities of different basic network architectures areanalyzed through a specially designed experiment, which could provide valuablesuggestions for network selection.

Hysteretic Behavior Simulation Based on Pyramid Neural Network:Principle, Network Architecture, Case Study and Explanation