본 논문에서는 사장교를 지탱하는 주요 부재인 케이블의 손상 위치를 빠르게 검출할 수 있는 손상평가 기술을 개발하고, 모형 교량 손상 실험을 통하여 개발한 기술의 손상평가 성능을 검증하고자 하였다. 손상평가 기술의 개발을 위하여 통계적 패턴 인식 기술인 마할라노비스 거리 이론을 활용하였으며, 복잡한 구조체의 손상위치 판별을 위하여 계측 위치별 획득 데이터의 변동성을 손상평가 기술에 반영하였다. 개발한 기술의 손상평가 성능을 확인하기 위하여 모형 사장교를 대상으로 손상 실험을 진행하였다. 그 결과, 개발한 손상평가 기술은 무손상 상태의 응답과 손상 상태의 응답을 활용하여 사장교 케이블 의 손상 위치를 검출할 수 있는 통계적 패턴을 제공하는 성능을 보이는 것을 확인하였다.

In this paper, damage assessment technology based on statistical pattern recognition technology was developed for maintenance of structure and the performance of the developed technology was verified by vibration test. The damage assessment technique uses the improved Mahalanobis distance theory, which is a statistical pattern recognition technique, and developed to take account of the variability between the measured data. In order to verify the damage evaluation performance of the developed technology, a cable damage test was conducted for a cable-stayed bridge. Experimental results show that the developed damage assessment technology has the capability of extracting information that can determine the location of damage due to cable damage.

In this paper, real-time damage assessment technology was developed for detection the damage of bridges in real time and the performance of the developed technology was verified by vibration test. Real-time damage assessment technology was developed by combining statistical pattern recognition technology and simulation technology. In order to verify the developed technology, the earthquake response acquisition experiment was conducted according to the cable damage of the model cable-stayed bridge. As a result, it was confirmed that the developed real-time damage assessment technology can provide information on the location of damaged cable.