结构状态识别与评估的机器学习方法研究进展

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doi:10.6052/1000-0992-23-018

哈尔滨工业大学土木工程学院, 150090 哈尔滨

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
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- PDF下载量:349
- 被引次数:0
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

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

School of Civil Engineering, Harbin Institute of Technology, Harbin 150090, China

More Information

Corresponding author:baoyuequan@hit.edu.cn

摘要

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.
- 结构损伤识别/
- 模态识别/
- 安全评定/
- 机器学习/
- 结构健康监测
Abstract:Structural health monitoring (SHM) has become an important technique to ensure the safety of major engineering structures by sensing, collecting, transmitting and processing multivariate data, through the installation of multiple types of sensors on large engineering structures. With the wide application of SHM system, a huge amount of monitoring data is generated, and how to identify and evaluate the structural condition and safety through monitoring data is one of the core scientific problems. Due to the complexity of civil engineering structures, the core difficulty of state identification and assessment is the optimization and solution of high-dimensional problems. Machine learning has a strong capability in solving high-dimensional problems, providing new ideas for the solution of this problem. This paper focuses on the research progress of machine learning in structural modal identification, damage identification and reliability assessment, and discusses the future development trend in these research directions.
- structural health monitoring/
- modal identification/
- damage identification/
- reliability evaluation/
- machine learning

HTML全文

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

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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)

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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)

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图 4结构模态参数聚类结果 (Fan et al. 2019)

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图 5结构损伤识别问题的层次贝叶斯模型

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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

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图 7结构损伤识别稠密卷积网络 (Wang et al. 2021b)

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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)

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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

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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)

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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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