Statistical Clustering and Times Series Analysis for Bridge Monitoring Data

Abstract

The process of implementing a damage detection strategy for bridges is referred to as Bridge Health Monitoring (BHM). The BHM process involves the observation of a system over time using periodically sampled dynamic response measurements from an array of sensors, the extraction of damage-sensitive features from these measurements, and the statistical analysis of these features to determine the current state of the system’s health [12]. Therefore, the achieved data from attached sensors would be very huge in dimensions, would make researchers confused in further examinations on data bridge. There have been many approaches to solve the BHM sensors reduction problem, range from univariate analysis between couples of variables [13] to carefully selecting measurement points based on specific bridge knowledge [7]. However, they are either inapplicable for interrelated nature data sets, or using too much mechanical knowledge in its process.