운용모드해석에 기반한 사장교의 장단기 동특성 평가
Evaluation of Short and Long-Term Modal Parameters of a Cable-Stayed Bridge Based on Operational Modal Analysis
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초록
상시진동을 이용하여 구조계의 동특성을 추출하는 운용모드해석 기법은 케이블교량 구조건전성모니터링의 한 분야로써 다양한 연구와 실험적 검증이 수행되어왔다. 본 연구에서는 두 번에 걸친 상시진동실험과 함께 3년간의 장기 계측을 통해 수집된 가속도 데이터를 이용하여 공용 중인 사장교의 장단기 동특성을 평가하였다. 교량 준공 이후 6년과 19년이 경과한 시기에 실시한 고해상도 상시진동실험으로부터 0.1 ~ 2.5 Hz 대역에서 27개 수직모드(휨, 비틈)와 1개 수평모드를 추출하였다. 운용모드해석에 기반한 동특성 추출은 PP기법, ERADC기법, FDD기법, TDD기법을 적용하였으며, 적용한 기법들 간에 유의미한 차이가 없는 것을 확인하였다. 장기 계측 고유진동수와 환경 요인(온도, 바람)에 대한 상관성 분석으로부터 온도 변화가 고유진동수 변동에 지배적인 영향인자임을 확인하였다. 대상교량의 고유진동수 감소 경향은 구조성능과 일체성이 변한 것이 아니라 두 번의 상시진동실험 간 온도 차이에 의한 환경영향이 컸음을 밝혔다. 또한 TDD기법 적용 시, 지연이 0에서 자기상관이 1이 되도록 시퀀스를 정규화하는 알고리즘을 추가하여 모드형상 추출의 정확도를 개선하였다.
Abstract
The operational modal analysis (OMA) technique, which extracts the modal parameters of a structural system using ambient vibrations, has been actively developed as a field of structural health monitoring of cable-supported bridges. In this paper, the short and long-term modal parameters of a cable-stayed bridge were evaluated using the acceleration data obtained from the two ambient vibration tests (AVTs) and three years of continuous measurements. A total of 27 vertical modes and 1 lateral mode in the range 0.1 ~ 2.5 Hz were extracted from the high-resolution AVTs which were conducted in the 6th and 19th years after its completion. Existing OMA methods such as Peak-Picking (PP), Eigensystem Realization Algorithm with Data Correlation (ERADC), Frequency Domain Decomposition (FDD) and Time Domain Decomposition (TDD) were applied for modal parameters extraction, and it was confirmed that there was no significant difference between the applied methods. From the correlation analysis between long-term natural frequencies and environmental factors, it was confirmed that temperature change is the dominant factor influencing natural frequency fluctuations. It was revealed that the decreased natural frequencies of the bridge were not due to changes in structural performance and integrity, but to the environmental effects caused by the temperature difference between the two AVTs. In addition, when the TDD technique is applied, the accuracy of extracted mode shapes is improved by adding a proposed algorithm that normalizes the sequence so that the autocorrelations at zero lag equal 1.
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