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

TL;DR

This paper introduces a novel pyramid neural network architecture with weighted stacking for simulating hysteretic material behavior, improving accuracy and efficiency over existing models through multi-level feature integration.

Contribution

The paper proposes a weighted stacked pyramid neural network with multi-level shortcuts, enhancing feature fusion and prediction accuracy for hysteretic behavior simulation.

Findings

Redesigned architecture outperforms alternatives in 87.5% of cases.

Enhanced feature fusion improves simulation accuracy.

Analysis of network memory abilities informs better network selection.

Abstract

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.