• Structural Engineering and Mechanics
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• 제54권2호
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• pp.337-362
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• 2015

This paper presents a feasibility study on structural damage alarming and localization of long-span cable-supported bridges using multi-novelty indices formulated by monitoring-derived modal parameters. The proposed method which requires neither structural model nor damage model is applicable to structures of arbitrary complexity. With the intention to enhance the tolerance to measurement noise/uncertainty and the sensitivity to structural damage, an improved novelty index is formulated in terms of auto-associative neural networks (ANNs) where the output vector is designated to differ from the input vector while the training of the ANNs needs only the measured modal properties of the intact structure under in-service conditions. After validating the enhanced capability of the improved novelty index for structural damage alarming over the commonly configured novelty index, the performance of the improved novelty index for damage occurrence detection of large-scale bridges is examined through numerical simulation studies of the suspension Tsing Ma Bridge (TMB) and the cable-stayed Ting Kau Bridge (TKB) incurred with different types of structural damage. Then the improved novelty index is extended to formulate multi-novelty indices in terms of the measured modal frequencies and incomplete modeshape components for damage region identification. The capability of the formulated multi-novelty indices for damage region identification is also examined through numerical simulations of the TMB and TKB.

• 한국구조물진단유지관리공학회 논문집
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• 제14권1호
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• pp.165-174
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• 2010

본 논문에서는 실물 콘크리트 거더 교량의 가속도 응답 신호를 이용하여 구조물의 상태변화를 경보한 후 그 위치 변화를 검색하는 2단계 구조건전성 모니터링 체계를 제시하였다. 먼저, 2경간 연속 콘크리트 거더 교량인 미호천교를 대상교량으로 선정하였으며, 볼링공을 이용한 강제진동 실험으로부터 동특성을 추출하였다. 다음으로, 미호천교의 2단계 구조건전성 모니터링 체계 구축을 위한 손상 발생 경보 및 손상 위치 검색 기법들을 선정하였다. 손상 경보 기법으로는 시간영역 특징을 이용하는 자기회귀모델과 주파수응답함수의 상관계수, 주파수응답비보증지수를 선정하였다. 손상 위치 검색 기법으로는 모드변형에너지기반 손상지수법을 선정하였다. 마지막으로, 덤프트럭을 이용한 정적 재하 실험을 통해 2단계 손상 모니터링 체계의 적합성을 검증하였다.

• 한국전산구조공학회논문집
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• 제25권1호
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• pp.81-89
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• 2012

본 연구에서는 무선 가속도 센서노드를 이용한 강 거더 볼트연결 부재의 진동기반 손상 모니터링 체계를 제안하였다. 이 같은 연구목표를 위해, 다음과 같은 연구를 수행하였다. 먼저, 무선 가속도 센서노드의 하드웨어 구성 및 내장된 작동 소프트웨어를 제시하였다. 다음으로, 강 거더 볼트연결 부재의 진동기반 손상 모니터링 체계를 제시하였다. 손상 모니터링 체계는 가속도 응답특성 분석을 통해 전역적 손상발생 경보 및 손상위치 추정을 수행한다. 전역적 손상발생 경보는 파워스펙트럼밀도의 상관계수를 적용하였다. 손상위치 추정은 고유 진동수기반 손상검색 기법과 모드형상기반 손상검색 기법을 적용하였다. 마지막으로, 모형 강 거더의 볼트연결 부재 손상을 식별하기 위한 진동기반 손상 모니터링 체계의 적용성을 검증하였다.

• Structural Monitoring and Maintenance
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• 제2권1호
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• pp.77-93
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• 2015

The idea of using measured dynamic characteristics for damage detection is attractive because it allows for a global evaluation of the structural health and condition. However, vibration-based damage detection for complex structures such as long-span cable-supported bridges still remains a challenge. As a suspension or cable-stayed bridge involves in general thousands of structural components, the conventional damage detection methods based on model updating and/or parameter identification might result in ill-conditioning and non-uniqueness in the solution of inverse problems. Alternatively, methods that utilize, to the utmost extent, information from forward problems and avoid direct solution to inverse problems would be more suitable for vibration-based damage detection of long-span cable-supported bridges. The auto-associative neural network (ANN) technique and the probabilistic neural network (PNN) technique, that both eschew inverse problems, have been proposed for identifying and locating damage in suspension and cable-stayed bridges. Without the help of a structural model, ANNs with appropriate configuration can be trained using only the measured modal frequencies from healthy structure under varying environmental conditions, and a new set of modal frequency data acquired from an unknown state of the structure is then fed into the trained ANNs for damage presence identification. With the help of a structural model, PNNs can be configured using the relative changes of modal frequencies before and after damage by assuming damage at different locations, and then the measured modal frequencies from the structure can be presented to locate the damage. However, such formulated ANNs and PNNs may still be incompetent to identify damage occurring at the deck members of a cable-supported bridge because of very low modal sensitivity to the damage. The present study endeavors to enhance the damage identification capability of ANNs and PNNs when being applied for identification of damage incurred at deck members. Effort is first made to construct combined modal parameters which are synthesized from measured modal frequencies and modal shape components to train ANNs for damage alarming. With the purpose of improving identification accuracy, effort is then made to configure PNNs for damage localization by adapting the smoothing parameter in the Bayesian classifier to different values for different pattern classes. The performance of the ANNs with their input being modal frequencies and the combined modal parameters respectively and the PNNs with constant and adaptive smoothing parameters respectively is evaluated through simulation studies of identifying damage inflicted on different deck members of the double-deck suspension Tsing Ma Bridge.