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基于ANN-PSO的不规则RC结构的设计优化
1School of Management, Guangzhou University, Guang Zhou, 510006, GuangDong, China.
Heliyon
|March 15, 2024
概括
这项研究优化了不规则钢筋混凝土 (RC) 框架的地震安全性. 人工神经网络-粒子群集优化 (ANN-PSO) 方法将扭曲不规则性降至最低,确保更安全,更经济的抗地震结构.
科学领域:
- 结构工程 结构工程
- 地震工程的工程是地震工程.
- 计算力学 计算力学 计算力学
背景情况:
- 地震设计优先考虑结构规律性以减轻地震损害,但这可能会限制设计美学和独特性.
- 工程师有时会选择不规则的结构,引入潜在的地震风险,特别是扭曲不规则.
- 优化不规则结构的设计对于提高地震活跃地区的安全性和功能性至关重要.
研究的目的:
- 为了实现平面不规则钢筋混凝土 (RC) 框架的扭矩不规则系数的最佳设计.
- 在结构设计中研究人工神经网络粒子群集优化 (ANN-PSO) 算法的有效性.
- 确保符合扭矩规律性标准,以实现更安全的地震性能.
主要方法:
- 通过ETABS软件使用3D6层模型进行结构地面振动分析.
- 应用了结合的人工神经网络 (ANN) 和粒子群优化 (PSO) 算法 (ANN-PSO) 来优化列尺寸.
- 对扭矩不规则系数和分析的静态和动态负载设置了限制.
主要成果:
- 该ANN-PSO算法有效优化了列横截面积,导致了显著的变化.
- 优化的扭矩不规则系数接近最低值,满足Y方向要求并防止不规则.
- 优化的柱子尺寸和方向与初始设计略有差异,提高了结构性能.
结论:
- 该研究提出了一种有效的方法,将ANN-PSO和有限元素方法 (FEM) 结合起来,用于设计正规的RC结构.
- 拟议的方法为满足扭矩规律性标准提供了一个实际的解决方案,使经济和安全的设计成为可能.
- 这一创新的框架为不规则的RC结构提供了最佳和安全的设计,最大限度地减少地震期间的扭伤损伤.
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