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Updated: Feb 14, 2026

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增强主动学习高斯过程元模型用于估计非线性结构响应的单边尾部概率
Yunzhe Wang1, Yanwen Huang2, Yihang Huang3
1Department of Planning and Construction, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China.
Scientific reports
|February 12, 2026
概括
一个新的Tail-Sensitive Global Learning (TS-GL) 算法改善了对大型结构的罕见事件概率估计. 该方法通过在准确性和效率方面超越现有技术来增强关键基础设施的安全分析.
科学领域:
- 结构工程 结构工程
- 计算力学 计算力学 计算力学
- 风险评估 风险评估
背景情况:
- 巨型结构面临的安全挑战来自罕见的,高影响的故障.
- 难以准确估计低概率的结构反应.
- 地下结构的故障,比如地铁道,可能是由于固定长度不足造成的.
研究的目的:
- 为准确估计结构工程中的罕见事件概率开发一种新的框架.
- 改进对结构性响应分布的一面尾部概率的估计.
- 为关键基础设施安全的不确定性分析提供一个实用的工具.
主要方法:
- 引入尾部敏感的全球学习 (TS-GL) 算法.
- TS-GL具有以尾部为重点的搜索机制和新的重量功能.
- 对计算效率的激活函数的研究.
主要成果:
- 与现有的方法相比,TS-GL显著改善了对单面尾巴概率的估计.
- 该算法在钢混凝土连接中的结合-滑动关系上得到了验证.
- 在稀有事件量化方面,TS-GL表现出比基于主动学习的高斯过程 (AL-GP) 的元模型更高的准确性和效率.
结论:
- TS-GL算法为关键基础设施的不确定性分析提供了实用和有效的解决方案.
- 这种新的框架提高了大型结构的安全运行和维护.
- 改善对罕见事件概率的估计对于防止结构性故障至关重要.
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