• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1997년도 봄 학술발표회 논문집
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• pp.97-105
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• 1997

A methodology to identify a baseline modal model of a complicated 3-D structure using limited structural and modal information is experimentally examined. In the first part, a system's identification theory for the methodology to identify, baseline modal responses of the structure is outlined. Next, an algorithm is designed to build a generic finite element model of the baseline structure and to calibrate the model by using only a set of post-damage modal parameters. In the second part, the feasibility of the methodology is examined experimentally using a field-tested truss bridge far which only post-damaged modal responses were measured for a few vibration modes. For the complex 3-D bridge with many members, we analyzed to identify unknown stiffness parameters of the structure by using modal parameters of the initial two modes of vibration.

• Smart Structures and Systems
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• 제24권2호
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• pp.243-256
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• 2019

Determination of the most meaningful structural modes and gaining insight into how these modes evolve are important issues for long-term structural health monitoring of the long-span bridges. To address this issue, modal parameters identified throughout the life of the bridge need to be compared and linked with each other, which is the process of mode tracking. The modal frequencies for a long-span bridge are typically closely-spaced, sensitive to the environment (e.g., temperature, wind, traffic, etc.), which makes the automated tracking of modal parameters a difficult process, often requiring human intervention. Machine learning methods are well-suited for uncovering complex underlying relationships between processes and thus have the potential to realize accurate and automated modal tracking. In this study, Gaussian mixture model (GMM), a popular unsupervised machine learning method, is employed to automatically determine and update baseline modal properties from the identified unlabeled modal parameters. On this foundation, a new mode tracking method is proposed for automated mode tracking for long-span bridges. Firstly, a numerical example for a three-degree-of-freedom system is employed to validate the feasibility of using GMM to automatically determine the baseline modal properties. Subsequently, the field monitoring data of a long-span bridge are utilized to illustrate the practical usage of GMM for automated determination of the baseline list. Finally, the continuously monitoring bridge acceleration data during strong typhoon events are employed to validate the reliability of proposed method in tracking the changing modal parameters. Results show that the proposed method can automatically track the modal parameters in disastrous scenarios and provide valuable references for condition assessment of the bridge structure.

• 한국강구조학회 논문집
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• 제10권2호통권35호
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• pp.279-290
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• 1998

소수의 진동특성치가 실측된 삼차원 트러스 교량을 대상으로 기존의 손상추정법이 실험되었다. 첫 번째 단계로 진동모드 민감도 해석과 패턴인식기법을 사용하여 초기구조모델(baseline model)이 구성되었고, 다음 단계로 수개의 손상시나리오 수치 예를 초기구조물에 시뮬레이션하고 이를 손상지수와 패턴인식기법을 이용하여 손상위치를 예측하였다. 총 211개 요소에 11개의 부 구조계를 갖는 트러스 구조에 대하여 진동모드가 2개인 경우에 한하여 분석 검토한 결과 손상발견 알고리즘의 적합성이 입증되었다.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2001년도 가을 학술발표회 논문집
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• pp.109-116
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• 2001

An integrated damage identification system (IDIS) and system identification (SID) technique using modal information to detect damage in structures is presented. The objective is to detect damages in cracked model plate-girder without baseline modal parameters. The theory of damage localization and system identification is outlined. Experiments on a model plate-girder was described and a baseline model representing the experimental modal characteristics of the model plate-girder is updated using the system identification technique. Finally, damage inflicted in the model plate-girder is predicted using the IDIS software.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2003년도 봄 학술발표회 논문집
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• pp.443-450
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• 2003

System identification techniques can be used to build a baseline modal model for a flawed structure that has no modal information on its as-built state. The accuracy of a system identification proposed by Stubbs and Kim is analyzed for plate-girder bridges and its impact on the accuracy of damage detection in those structures is also analyzed. A laboratory-scale model plate-girder is experimentally tested and the initial four bending modes are examined for certain damage scenarios. The performance of individual baseline modal models is assessed by detecting damage in the model structure.

• Smart Structures and Systems
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• 제26권3호
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• pp.291-309
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• 2020

To study oceanic and meteorological problems related to climate change, Korea has been operating several ocean research stations (ORSs). In 2011, the Gageocho ORS was attacked by Typhoon Muifa, and its structural members and several observation devices were severely damaged. After this event, the Gageocho ORS was rehabilitated with 5 m height to account for 100-yr extreme wave height, and the vibration measurement system was equipped to monitor the structural vibrational characteristics including natural frequencies and modal damping ratios. In this study, a mass reallocation method is presented for structural model updating of the Gageocho ORS based on the experimentally identified natural frequencies. A preliminary finite element (FE) model was constructed based on design drawings, and several of the candidate baseline FE models were manually built, taking into account the different structural conditions such as corroded thickness. Among these candidate baseline FE models, the most reasonable baseline FE model was selected by comparing the differences between the identified and calculated natural frequencies; the most suitable baseline FE model was updated based on the identified modal properties, and by using the pattern search method, which is one of direct search optimization methods. The mass reallocation method is newly proposed as a means to determine the equivalent mass quantities along the height and in a floor. It was found that the natural frequencies calculated based on the updated FE model was very close to the identified natural frequencies. In conclusion, it is expected that these results, which were obtained by updating a baseline FE model, can be useful for establishing the reference database for jacket-type offshore structures, and assessing the structural integrity of the Gageocho ORS.

• 한국산학기술학회논문지
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• 제21권6호
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• pp.218-226
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• 2020

해상풍력터빈 하부구조물은 중요한 기능의 수행, 접근성의 제약 등으로 인하여 건전성 모니터링을 통한 효과적 유지관리가 필요하다. 본 연구에서는 해상풍력터빈 트라이포드 하부구조물의 건전성 모니터링을 위한 기저모델개선 및 결함추정 기법을 실험적으로 연구한다. 우선 하부구조물 건전성 모니터링을 위한 절차를 제안한 후 이 과정을 트라이포드 하부구조물 축소모형에 대하여 적용한다. 즉, 축소모형에 대한 초기 기저모델을 수치적으로 수립한 후 모드특성을 추정하고, 건전상태 진동실험 결과로부터 구한 고유주파수와 모드형상을 기준으로 기저모델을 개선하는데, 이때 구조물의 경계조건을 고려하고 신경망기법을 이용한다. 이후, 개선된 기저모델을 이용하여 신경망의 훈련패턴을 생성하고, 손상상태 진동실험 결과로부터 구한 모드특성을 훈련된 신경망에 입력함으로써 결함을 추정한다. 유효고정부 모델을 이용하여, 건전상태에서 측정된 모드특성에 맞추어 합리적으로 기저모델을 수립할 수 있었다. 또한, 축소모형에 대한 손상실험을 수행하였는데, 4가지 손상경우에 대하여 손상을 추정한 결과, 합리적으로 손상위치를 추정할 수 있었으며, 실제 손상정도가 심해질수록 손상정도 추정치도 증가하였다. 그러나 손상정도가 상대적으로 미소한 경우, 해당 손상위치가 판정은 되지만 다른 위치와 비교하여 확실한 손상위치의 식별이 어려웠다. 향후, 이러한 미소손상 추정 및 손상정도 추정치의 강성감소에 대한 정량화 등에 대한 후속연구가 수반된다면, 해상풍력터빈 트라이포드 하부구조물의 건전성 모니터링에 제안 기법을 효과적으로 활용할 수 있을 것으로 판단된다.

• Structural Engineering and Mechanics
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• 제29권3호
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• pp.311-325
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• 2008

Noting that damage occurrence of offshore jacket platforms is concentrated in two structural regions that are in the vicinity of still water surface and close to the seabed, a damage detection method by using only partial measurement of vibration in a suspect region was presented in this paper, which can not only locate damaged members but also evaluate damage severities. Then employing an experiment platform model under white-noise ground excitation by shaking table and using modal parameters of the first three modes identified by a scalar-type ARMA method on undamaged and damaged structures, the feasibility of the damage detection method was discussed. Modal parameters from eigenvalue analysis on the structural FEM model were also used to help the discussions. It is demonstrated that the damage detection algorithm is feasible on damage location and severity evaluation for broken slanted braces and it is robust against the errors of baseline FEM model to real structure when the principal errors is formed by difference of modal frequencies. It is also found that Z-value changes of modal shapes also play a role in the precise detection of damage.

• Structural Monitoring and Maintenance
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• 제3권4호
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• pp.349-375
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• 2016

The tension of an arch bridge hanger is estimated using a number of experimentally identified modal frequencies. The hanger is connected through metallic plates to the bridge deck and arch. Two different categories of model classes are considered to simulate the vibrations of the hanger: an analytical model based on the Euler-Bernoulli beam theory, and a high-fidelity finite element (FE) model. A Bayesian parameter estimation and model selection method is used to discriminate between models, select the best model, and estimate the hanger tension and its uncertainty. It is demonstrated that the end plate connections and boundary conditions of the hanger due to the flexibility of the deck/arch significantly affect the estimate of the axial load and its uncertainty. A fixed-end high fidelity FE model of the hanger underestimates the hanger tension by more than 20 compared to a baseline FE model with flexible supports. Simplified beam models can give fairly accurate results, close to the ones obtained from the high fidelity FE model with flexible support conditions, provided that the concept of equivalent length is introduced and/or end rotational springs are included to simulate the flexibility of the hanger ends. The effect of the number of experimentally identified modal frequencies on the estimates of the hanger tension and its uncertainty is investigated.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2003년도 봄 학술발표회 논문집
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• pp.382-387
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• 2003

In this paper, a neural networks-based damage detection method using the modal properties is presented, which can effectively reduce the effect of the modeling errors in the baseline finite element model from which the training patterns for the networks are to be generated. The differences or the ratios of the mode shape components between before and after damage are used as the input to the neural networks in this method, since they are found to be less sensitive to the modeling errors than the mode shapes themselves. Results of laboratory test on a simply supported bridge model and field test on a bridge with multiple girders confirm the applicability of the present method.