单层圆顶的结构优化使用代用基于物理的神经网络
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Updated: Jul 12, 2025
14:23
Design and Optimization Strategies of a High-Performance Vented Box
Published on: June 9, 2023
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Hongyu Wu1, Yu-Ching Wu1, Peng Zhi1
1Department of Structural Engineering, College of Civil Engineering, Tongji University, Shanghai, China.
Heliyon
|October 27, 2023
概括
这项研究引入了一种新的人工蜂群算法,该算法使用神经网络代用有限元素方法. 这种方法显著加快了单层圆顶结构最佳设计的计算效率.
科学领域:
- 结构工程 结构工程
- 计算力学 计算力学 计算力学
- 在工程领域的人工智能.
背景情况:
- 结构设计中的几何非线性优化问题是计算密集的.
- 传统的有限元分析 (FEA) 可以成为复杂的优化任务的瓶.
- 开发高效的替代模型对于加速结构设计过程至关重要.
研究的目的:
- 开发一种新的人工蜂群算法,与使用神经网络的替代有限元素方法 (FEM) 集成.
- 应用这种增强的算法来解决单层圆顶的几何非线性优化问题,包括大小,形状和拓优化.
- 为了提高结构最佳设计的计算效率.
主要方法:
- 创建和应用一个代用有限元素方法,结合物理信息的神经网络 (PINNs).
- 在人工蜂群算法中使用前神经网络替代有限元分析.
- 通过数值示例进行验证: 10 条杆架,拉梅拉圆顶和基维特圆顶.
主要成果:
- 提出的方法证明了可行性和准确性,结果与现有文献非常一致.
- 通过修改的人工蜂群算法,优化过程得到了相当大的加速.
- 基于神经网络代理模型的模型显著提高了单层圆顶结构最佳设计的计算效率.
结论:
- 神经网络代理模型与人工蜂群算法的集成为高效的结构优化提供了强大的方法.
- 该方法有效地解决了单层圆顶设计中的几何非线性优化挑战.
- 这项研究突出了人工智能驱动的代理建模的潜力,可以彻底改变计算结构工程.
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