【摘要】 结构健康监测(SHM)是一个多学科交叉领域,涉及利用大量传感器和仪器对结构荷载和响应进行自动感知,然后根据收集到的数据对结构进行健康诊断。由于安装在结构上的SHM系统能自动实时地感知、评估和预警结构状态,所以海量数据是SHM的一个显著特征。与海量数据处理与分析相关的方法与技术被称为数据科学与工程,其包括数据采集、数据转换、数据管理以及数据处理与挖掘算法。本文旨在简要回顾笔者在SHM数据科学与工程方面开展的最新研究,具体涵盖基于压缩采样的数据采集算法、基于深度学习算法的异常数据诊断方法、基于计算机视觉技术的桥梁裂纹识别方法,以及基于机器学习算法的桥梁结构状态评估方法。最后,本文在结语部分对该领域的未来发展趋势进行了展望。更多还原

【Abstract】 Structural health monitoring(SHM) is a multi-discipline field that involves the automatic sensing of structural loads and response by means of a large number of sensors and instruments, followed by a diagnosis of the structural health based on the collected data. Because an SHM system implemented into a structure automatically senses, evaluates, and warns about structural conditions in real time, massive data are a significant feature of SHM. The techniques related to massive data are referred to as data science and engineering, and include acquisition techniques, transition techniques, management techniques, and processing and mining algorithms for massive data. This paper provides a brief review of the state of the art of data science and engineering in SHM as investigated by these authors, and covers the compressive sampling-based data-acquisition algorithm, the anomaly data diagnosis approach using a deep learning algorithm, crack identification approaches using computer vision techniques, and condition assessment approaches for bridges using machine learning algorithms. Future trends are discussed in the conclusion.更多还原