A major challenge in structural health monitoring (SHM) is the efficient handling of big data, namely of high-dimensional datasets, when damage detection under environmental variability is being assessed. To address this issue, a novel data-driven approach to early damage detection is proposed here. The approach is based on an efficient partitioning of the dataset, gathering the sensor recordings, and on classical multidimensional scaling (CMDS). The partitioning procedure aims at moving towards a low-dimensional feature space; the CMDS algorithm is instead exploited to set the coordinates in the mentioned low-dimensional space, and define damage indices through norms of the said coordinates. The proposed approach is shown to efficiently and robustly address the challenges linked to high-dimensional datasets and environmental variability. Results related to two large-scale test cases are reported: the ASCE structure, and the Z24 bridge. A high sensitivity to damage and a limited (if any) number of false alarms and false detections are reported, testifying the efficacy of the proposed data-driven approach.

中文翻译：

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一种基于数据划分和经典多维尺度的方法

结构健康监控（SHM）中的主要挑战是，当评估环境可变性下的损坏检测时，如何有效处理大数据（即高维数据集）。为了解决这个问题，这里提出了一种新颖的数据驱动的早期损坏检测方法。该方法基于数​​据集的有效划分，收集传感器记录以及经典的多维缩放（CMDS）。分割过程旨在向低维特征空间移动；而是利用CMDS算法在所述的低维空间中设置坐标，并通过所述坐标的范数来定义损伤指数。结果表明，所提出的方法可以有效而稳健地解决与高维数据集和环境变异性相关的挑战。报告了与两个大型测试案例有关的结果：ASCE结构和Z24桥。据报道，它对损坏具有很高的敏感性，并且虚假警报和虚假检测的数量有限（如果有的话），证明了所提出的数据驱动方法的有效性。