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Structural Damage Localization Using Sensor Cluster Based Regression Schemes

机译：基于传感器群集的回归方案的结构损坏定位

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Automatic damage identification from sensor measurements has long been a topic of interest in the civil engineering research community. A number of methods, including classical system identification and time series analysis techniques, have been proposed to detect the existence of damage in structures. Not many of them, though, are reported efficient for higher-level damage detection which concerns damage localization and severity assessment. In this paper, regression-based damage localization schemes are proposed and applied to signals generated from a simulated two-bay steel frame. These regression algorithms operates on substructural beam models, and uses the acceleration/strain responses at beam ends as input and the acceleration from an intermediate node as output. From the regression coefficients and residuals three damage identification features are extracted, and two change point analysis techniques are adopted to evaluate if a change of statistical significance occurred in the extracted feature sequences. For the four damage scenarios simulated, the algorithms identified the damage existence and partially succeeded in locating the damage. More accurate inferences on damage location are drawn by combining the results from different algorithms using a weighted voting scheme.
机译：传感器测量的自动损坏识别长期以来一直是土木工程研究界感兴趣的主题。已经提出了许多方法，包括经典系统识别和时间序列分析技术，以检测结构损坏的存在。但是，它们中的许多人报告了高级损伤检测有效，涉及损害本地化和严重程度评估。在本文中，提出了基于回归的损伤定位方案并应用于模拟双架钢框架产生的信号。这些回归算法在子结构波束模型上运行，并使用光束端的加速/应变响应作为输入和从中间节点的加速度作为输出。从回归系数和残差中提取三次损伤识别特征，采用两个改变点分析技术来评估提取的特征序列中发生统计显着性的变化。对于模拟的四种损伤情景，算法识别出存在损坏，部分地成功地定位损坏。通过使用加权投票方案将来自不同算法的结果组合来绘制对损坏位置的更准确的推断。

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