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结构状态识别与评估的机器学习方法研究进展

黄永 鲍跃全 李惠

黄永, 鲍跃全, 李惠. 结构状态识别与评估的机器学习方法研究进展. 力学进展, 2023, 53(4): 774-792 doi: 10.6052/1000-0992-23-018
引用本文: 黄永, 鲍跃全, 李惠. 结构状态识别与评估的机器学习方法研究进展. 力学进展, 2023, 53(4): 774-792 doi: 10.6052/1000-0992-23-018
Huang Y, Bao Y Q, Li H. Research advances in machine learning for structural state identification and condition assessment. Advances in Mechanics, 2023, 53(4): 774-792 doi: 10.6052/1000-0992-23-018
Citation: Huang Y, Bao Y Q, Li H. Research advances in machine learning for structural state identification and condition assessment. Advances in Mechanics, 2023, 53(4): 774-792 doi: 10.6052/1000-0992-23-018

结构状态识别与评估的机器学习方法研究进展

doi: 10.6052/1000-0992-23-018
基金项目: 国家自然科学基金 (51921006, 51978216, U2139209, 52192664) 资助项目.
详细信息
    作者简介:

    鲍跃全, 哈尔滨工业大学土木工程学院教授, 国家级青年人才, 曾在美国加州理工学院留学两年, 长期从事结构健康监测数据科学与工程方向研究. 发表SCI论文50余篇, 入选ESI高被引论文6篇, 出版专著和教材3部, 参编规范5部; 研究成果应用于20余座重大工程, 获国家科技进步二等奖(5/10)、黑龙江省自然科学一等奖 (3/5); 入选2021、2022年度斯坦福大学发布的全球前2%顶尖科学家; 任国际智能基础设施结构健康监测学会(ISHMII)理事等

    通讯作者:

    baoyuequan@hit.edu.cn

  • 中图分类号: O327

Research advances in machine learning for structural state identification and condition assessment

More Information
  • 摘要: 结构健康监测通过在大型工程结构上安装多类型传感器, 感知、采集、传输和处理多元数据, 已经成为保障重大工程结构安全的重要手段. 随着结构健康监测系统的广泛应用, 产生了海量的监测数据, 如何通过监测数据识别和评估结构状态与安全是核心科学问题之一. 由于土木工程结构的复杂性, 状态识别与评估的核心难点是高维问题优化与求解, 机器学习在高维问题求解方面具有很强的能力, 为该问题的解决提供了新的思路. 本文重点阐述机器学习在结构模态识别、损伤识别及可靠性评估等方面的研究进展, 并讨论未来在该研究方向的发展趋势.

     

  • 图  1  基于计算机视觉的结构模态参数识别方法 (Yang et al. 2017a)

    图  2  基于模态独立性的结构模态参数机器学习求解方法 (Liu et al. 2021)

    图  3  基于机器学习和随机子空间的结构模态参数识别方法 (Liu et al. 2023)

    图  4  结构模态参数聚类结果 (Fan et al. 2019)

    图  5  结构损伤识别问题的层次贝叶斯模型

    图  6  结构损伤识别吉布斯采样的后验分布样本. (a)某损伤单元和未损伤单元刚度参数的联合后验分布样本, (b) 某两个未损伤单元刚度参数的联合后验分布样本

    图  7  结构损伤识别稠密卷积网络 (Wang et al. 2021b)

    图  8  结构损伤识别稠密卷积网络. (a) 主梁位移群和拉索索力群的时空概率分布相关深度学习模型, (b) 基于索力真实和预测分布Wasserstein距离的斜拉索损伤识别 (Xu et al. 2023)

    图  9  基于自博弈策略和深度强化学习的结构系统主要失效模式搜索方法. (a)方法流程, (b)桁架结构主要失效模式搜索结果

    图  10  基于可解释深度生成网络的结构可靠度重要抽样. (a)可解释深度生成网络重要抽样模型, (b)输出样本极限状态函数值, (c)输出样本概率分布 (Xiang et al. 2023)

    图  11  基于深度强化学习的结构可靠度分析抽样. (a) 可解释深度生成网络重要抽样模型, (b) 强化学习选择的训练样本, (c) 对比方法选择的训练样本 (Xiang et al. 2020a)

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出版历程
  • 收稿日期:  2023-05-23
  • 录用日期:  2023-12-15
  • 网络出版日期:  2023-12-19
  • 刊出日期:  2023-12-30

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