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Identification of unusual events in multi-channel bridge monitoring data

机译：识别多通道桥梁监控数据中的异常事件

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Continuously operating instrumented structural health monitoring (SHM) systems are becoming a practical alternative to replace visual inspection for assessment of condition and soundness of civil infrastructure such as bridges. However, converting large amounts of data from an SHM system into usable information is a great challenge to which special signal processing techniques must be applied. This study is devoted to identification of abrupt, anomalous and potentially onerous events in the time histories of static, hourly sampled strains recorded by a multi-sensor SHM system installed in a major bridge structure and operating continuously for a long time. Such events may result, among other causes, from sudden settlement of foundation, ground movement, excessive traffic load or failure of post-tensioning cables. A method of outlier detection in multivariate data has been applied to the problem of finding and localising sudden events in the strain data. For sharp discrimination of abrupt strain changes from slowly varying ones wavelet transform has been used. The proposed method has been successfully tested using known events recorded during construction of the bridge, and later effectively used for detection of anomalous post-construction events.
机译：连续运行的仪器化结构健康监测（SHM）系统正成为一种替代视觉检查的实用替代方法，用于评估桥梁等民用基础设施的状况和健全性。但是，将大量数据从SHM系统转换为可用信息是一个巨大的挑战，必须应用特殊的信号处理技术。这项研究致力于识别由安装在主要桥梁结构中并长时间连续运行的多传感器SHM系统记录的每小时采样的静态静态应变的时间历史中的突变，异常和潜在繁重的事件。除其他原因外，此类事件可能由地基的突然沉降，地面运动，过大的交通负荷或后张紧电缆的故障引起。在多元数据中的异常值检测方法已被应用于发现和定位应变数据中突发事件的问题。为了从缓慢变化的应变中清晰地区分突变应变，已经使用了小波变换。使用桥梁施工过程中记录的已知事件已成功测试了所提出的方法，后来有效地用于检测异常的施工后事件。

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