在混合不确定性下对FRP托斯结构进行基于元模型的多尺度可靠性分析
Desheng Zhao1,2, Xiaoyi Zhou1, Wenqing Wu1
1Department of Bridge Engineering, School of Transportation, Southeast University, Nanjing 211189, China.
Materials (Basel, Switzerland)
|January 11, 2024
概括
本研究提出了一种新的算法,用于分析纤维增强聚合物 (FRP) 结构的可靠性,其不确定性是随机的,也是间隔的. RBF-GA-BP-IS方法准确地估计了故障概率,改进了结构分析.
科学领域:
- 结构工程 结构工程
- 材料科学 材料科学 材料科学
- 计算力学 计算力学 计算力学
背景情况:
- 纤维增强聚合物 (FRP) 复合材料越来越多地用于结构应用.
- 具有混合不确定性的复杂结构的可靠性分析在计算上具有挑战性.
- 现有的方法可能无法充分解决随机和间隔参数的综合影响.
研究的目的:
- 引入一种新的算法 - - 射线基函数-遗传算法-反向传播-重要性采样 (RBF-GA-BP-IS),用于FRP结构的多尺度可靠性分析.
- 为了有效地处理混合不确定性,包括随机变量和间隔参数.
- 提高FRP复合结构可靠性评估的准确性.
主要方法:
- 辐射基函数 (RBF) 神经网络,遗传算法 (GA),逆向传播 (BP) 神经网络和重要性采样 (IS) 的集成.
- 开发一个框架来解决可靠性分析中的内部和外部优化问题.
- 将算法应用于各种结构模型,包括平面托架,圆顶托架和托架桥梁.
主要成果:
- 在模型调用数量方面,RBF-GA-BP-IS算法表现出高效率.
- 实现了FRP结构故障概率的准确估计.
- 从基准失败概率的只有0.08%的偏差被观察到在七条平面结构结构评估.
结论:
- 拟议的RBF-GA-BP-IS算法对于具有混合不确定性的FRP结构的多尺度可靠性分析是有效的.
- 该方法通过在定义的边界内考虑波动的参数来提高分析精度.
- 算法的性能验证了它对复杂和非线性结构系统的适用性.
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