Structural Health Monitoring Methodology based on Outlier Analysis using Acceleration of Subway Stations

가속도 응답을 이용한 이상치 해석 기반 역사 구조 건전성 평가 기법 개발

Station structures, one of important infrastructures, which have been being operated since the 1970s, are especially vulnerable to even the medium-level earthquake and they could be damaged by long-term internal or external vibrations such as ambient vibrations. Recently, much attention has been paid to real-time monitoring of the fatal defect or long-term deterioration of civil infrastructures to ensure their safety and adequate performance throughout their life span. In this study, a structural health monitoring methodology using acceleration responses is proposed to evaluate the health-state of the station structures and to detect initial damage-stage. A damage index is developed using the acceleration data and it is applied to outlier analysis, one of unsupervised learning based pattern recognition methods. A threshold value for the outlier analysis is determined based on confidence level of the probabilistic distribution of the acceleration data. The probabilistic distribution is selected according to the feature of the collected data.