基于RBF神经网络和改进的海算法,优化大跨度有线桥梁中的电缆张力
Dan Zhao1, Hua Wang2,3, Mengsheng Yu4,5
1Guangxi University of Education, Nanning, 530023, China.
Scientific reports
|November 29, 2025
概括
这项研究引入了一种用于大跨度电缆桥梁的电缆力优化的新模型,使用辐射基函数神经网络和增强的海优化算法来提高可靠性. 该方法显著降低了桥梁的斜率,并提高了张力可靠性指标.
科学领域:
- 结构工程 结构工程
- 计算力学 计算力学 计算力学
- 可靠性工程可靠性工程
背景情况:
- 大跨度电缆站桥需要精确的电缆力优化,以确保结构完整性和可靠性.
- 现有的优化方法可能无法充分捕捉复杂的非线性和可靠性考虑.
研究的目的:
- 开发一种新的强力优化模型,用于大型跨度电缆桥梁,并结合可靠性指标.
- 提高结构响应预测和优化算法的准确性和效率.
主要方法:
- 建立了一个使用辐射基函数神经网络 (RBFNN) 进行非线性映射的结构替代模型.
- 改进了标准的海优化算法 (SOA),具有折射反向传播和非线性收.
- 集成的RBFNN和增强的SOA用于联合力优化方法.
主要成果:
- RBFNN有效地建模了结构性随机变量和动态响应之间的关系.
- 增强的SOA在优化方面表现出比标准SOA更好的性能.
- 优化减少了直径光线偏移高达36.21%,并提高了高达9%的张力可靠性指标.
结论:
- 拟议的RBFNN增强的SOA方法是有效的可靠的力量优化在大跨度电缆桥梁.
- 这种方法显著提高了结构性能,特别是在减少中跨度偏移方面.
- 该研究验证了将可靠性指标集成到电缆强度优化过程中的有效性.
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