Localized Structural Damage Detection: A Change Point Analysis

Many current damage detection techniques rely on the skill and experience of a trained inspector and also require a priori knowledge about the structure’s properties. However, this study presents adaptation of several change point analysis techniques for their performance in civil engineering damage detection. Literature shows different statistical approaches which are developed for detection of changes in observations for different applications including structural damage detection. However, despite their importance in damage detection, control charts and statistical frameworks are not properly utilized in this area. On the other hand, most of the existing change point analysis techniques were originally developed for applications in the stock market or industrial engineering processes; utilizing them in structural damage detection needs adjustments and verification. Therefore, in this article several change point detection methods are evaluated and adjusted for a damage detection scheme. The effectiveness of features from a statistics based local damage detection algorithm called Influenced Coefficient Based Damage Detection Algorithm (IDDA) is expanded for a more complex structural system. The statistics used in this study include the univariate Cumulative Sum, Exponentially Weighted Moving Average (EWMA), Mean Square Error (MSE), and multivariate Mahalanobis distances, and Fisher Criterion. They are used to make control charts that detect and localize the damage by correlating locations of a sensor network with the damage features. A Modified MSE statistic, called ModMSE statistic, is introduced to remove the sensitivity of the MSE statistic to the variance of a data set. The effectiveness of each statistic is analyzed.