Structural Damage Localization Using Sensor Cluster Based Regression Schemes

Abstract

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.