结构状态识别与评估的机器学习方法研究进展

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

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.

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.