A damage assessment technique based on a revised Statistical Pattern-recognition Technique (SPRT)

Abstract

Structural conditions assessment utilizes data analysis methods based on signals measured from subject structures. Among the various signal-based analysis methods available, a Statistical Pattern-recognition Technique (SPRT) based on Mahalanobis Distance (MD) theory is considered useful in assessing structural conditions; however, its evaluation of volatile signal data incurred by external loads such as seismic loads is usually limited. In this study, an experiment was conducted to attempt to overcome such limitations of MD theory. Furthermore, research was conducted on condition assessment methods based on SPRTs that can accurately assess structural conditions, even with highly volatile measured data. As a first step, the limitations of the standard MD theory were experimentally confirmed. Based on these limitations, a revised MD (RMD) theory was subsequently proposed. To verify the proposed SPRT, standardized cyclic loads and non-standardized seismic loads were used to conduct experiments on structural damage assessment. Based on these results, the RMD theory proposed in this study was confirmed to be a valid method for comprehending structural damage.